ABSTRACT. The industry presentation will provide key latest news from Multi-disciplinary and value creating international standards - latest news from ISO/TC67/WG4 Reliability engineering and technology. This work group is responsible for reliability and cost related ISO/TC 67 standardization activities for the entire Petroleum, petrochemical and natural gas industries.

The presentation will give highlights of the following ISO standards that can be applied by various stakeholders, also in other industries:
- ISO 14224: Collection and exchange of reliability and maintenance data for equipment
- ISO 20815: Production assurance and reliability management
- ISO/TR 12489: Reliability modelling and calculation of safety systems
- ISO 15663: Life cycle costing
- ISO 19008: Standard cost coding system to gas production and processing facilities

ABSTRACT. In 2014 the International Standardisation Organisation (ISO) responded to the increasing demand from asset managers to provide a structured and comprehensive standard to improve the effectiveness of assets within an organisation. The new standard ISO 55000 is comprehensive and detailed with capability for supporting the coordinated activities of an organisation in its quest to realise value from assets through, among others, improved financial performance and improved maintenance strategies. This allows the organisation to improve its decision-making, balance costs, risks and performance. The aim of this paper is to examine the compositions of ISO 55000 and identify the key elements necessary for its successful and economically feasible implementation. The study, is the last stage of a three year PhD programme, that consist of part of the research analysed data with asset managers and asset maintenance personal within SMEs to obtain a set of key issues which need to be addressed. In addition, the paper highlights the need for the development of an effective and efficient approach that makes the adoption and implementation of ISO 55000 suitable for small to medium enterprises. This new approach addresses several inherent constraints often faced by small companies when adopting new manufacturing and maintenance initiatives.

ABSTRACT. With the onset of the breakthrough innovations in Industry 4.0, digitalisation is expected to improve the value creation from industrial assets. A fundamental in Industry 4.0 is to virtually represent technical objects such as machines and production plants in cyber-physical systems (CPS). As a concrete Norwegian initiative based upon these opportunities, the on-going research project CPS Plant is expected to contribute to improved production performance both in manufacturing and production companies. CPS plant will develop and implement enabling technologies and methods for Norwegian Industries where CPS will integrate the virtual world with the physical world. To support this ambition for the Norwegian Industry, the development of a successful asset management strategy implementation in CPS Plant should be regarded as relevant. With a sound asset management strategy implemented, coordinated activities in the organisation should realize value from assets. Today an own framework for CPS Plant has been developed and is expected to be fundamental for further research activities in CPS Plant. The aim in this article is to propose an asset management strategy implementation roadmap that will support implementation of CPS plant in Norwegian manufacturing and production companies. The results in this article will be further tested and evaluated by Norwegian industry. In particular, the asset management strategy will be based on fundamental organisation theories as well as experiences from the project. The article concludes that all though the Asset Management Strategy Implementation will be tested in Norwegian companies, it remains to evaluate how this strategy should be “adjusted” for similar implementation in other countries.

ABSTRACT. Modern manufacturing organizations are designing, building and operating large, complex and often ‘one of a kind’ assets, which incorporate many different electrical, electronic, hydraulic and mechanical systems and components. Due to this complexity and the need to react quickly to changes in production, there is a need for more advanced strategies to ensure effective and efficient high maintenance and high availability. Modern maintenance strategies including Total Productive Maintenance and Reliability Centred Maintenance (RCM) have proven successful. With the increase in complexity and a large number of interconnected components, however, the use of one single strategy may not provide the necessary detailed system to support a maintenance task selection. The paper will propose that for complex assets with a large number of systems, a new framework will be proposed utilising RCM, and include additional supporting systems, including Value Stream Mapping and Cost Benefit Analysis.

ABSTRACT. The cost and profitability related to operation and maintenance phase is signifi-cant for several industrial applications. Thus, the first proactive maintenance management action is to design/out the maintenance work by designing out the critical failure modes and causes. The dependability standards (IEC 60300) pro-vides the framework and methodology to design for maintainability at project phase. However, the risk that physical asset will fail is always there as there are changes in the operating and loading conditions, which might initiate new failure modes. The philosophy of industry 4.0 is to develop smart asset to enable a real-time monitoring of the dynamic asset behaviour. In this context, the dependabil-ity standards (IEC 60300) need to be updated to consider the technical require-ments that support the intelligent maintenance process. Therefore, the purpose of this paper is to present a potential reference standard related to “design for intel-ligent maintenance” that comply with industry 4.0 requirements. This work illus-trates the progress toward a unified standard body for dependability in industry 4.0, which might lead to significant changes in the current state of the art in de-signing industrial assets. For example, among the 80,000 sensors that are at-tached to modern oil and gas platforms, a few ones are generating data for health monitoring and maintenance purposes, the majority applied for detecting opera-tional anomalies and control.

ABSTRACT. Assets are aging and budgets are tight, yet expectations are higher than ever. The challenge is that as you move to more detailed levels of the approach, the budgets, the authority to take action, and the performance metrics all start to become fragmented by organizational sub-units, asset service level requirements, regional requirements and compliance to regulatory and safety requirements. While potentially serving to motivate and provide realistic goals, it starts to undermine the value of a holistic asset management approach. If the decision making is not optimized across business asset portfolios and operational units, or even within business units, the value is diluted when all interactions between the assets and other factors are not adequately considered. With limited capital and operational expenditure (CAPEX/OPEX) budgets, utilities need to make hard decisions about where and when to spend their money to maximize returns on CAPEX while optimizing or deferring OPEX. Deterministic and probabilistic techniques can assist utilities to better justify CAPEX and OPEX budgets. They also can model these activities by evaluating business processes, asset condition and asset degradation to generate asset investment plans based on actual and predictive asset performance long into the future. This allows authorities and health asset operational agencies to effectively optimize costs, risks, opportunities and performance This paper provides an overview of the challenges and discusses various aspects of making better decisions to assist authorities and operating agencies of health services asset portfolios to balance costs, risks, opportunities and performance challenges. The paper highlights a structured, digitised and automated asset condition audit for assessment and maintaining the functional integrity and serviceability of the facility during the operating life. The paper also presents an overall view in using the inspection and site specific characteristic data for calibrations/validations of the original design and then to maintain the level of service and to develop an asset management budgeting and investment decision for portfolio of health services assets.

ABSTRACT. Climate change and the associated rise of the sea level is one of the major concerns for urban deltas, which house about half the population of the world. Part of the mitigation pack is decarbonizing the energy production, by means of renewable energy sources. These are geographically distributed by nature, putting a higher demand on the distribution grid. Because of regulatory pressures, most network operators connect them by means of an incremental strategy. However, decarbonizing the energy system requires electrification. This means the capacity of the grid has to increase with a factor 10 in 30 years. An incremental strategy is unlikely to result in such a growth, on the contrary is more likely to waste resource. In this paper we demonstrate the waste of resources by incremental strategies under strong growth scenarios with a case study of wind turbines in the Netherlands. Visionary strategies can outperform incremental strategies in these conditions by as much as 30%. We end the paper with an appeal to all asset managers to develop visionary strategic plans to support the coming energy transition.

ABSTRACT. Sustainability of assets and processes is a major strategic focus area for many companies. To achieve the strategic goals of sustainability, several companies have begun to discuss how their businesses are supporting circular economy. Digi-tal technologies and data are considered key enablers of circular economy. Circu-lar economy solutions include e.g. minimizing the use of resources, closing re-source loops and improving durability and lifetimes of assets. This paper discusses the impact of circular economy on asset management. The focus is especially on the different representations of lifecycles in production systems. We discuss the challenges and opportunities related to generic, circular and hierarchic production system lifecycles. We present main points to be considered when developing circu-lar economy solutions from the perspective of asset and lifecycle management.

ABSTRACT. Nature’s ecosystem and its services creates the foundation of our society’s existence. Yet, services such as fresh air and water, fertile soil, and flow regulation etc. have upon recent been treated as unlimited sources of goods. The consequences of this can be seen in the rapid loss of biodiversity, and issues related to climate change that societies now are facing.

In order to study the significance of including environmental goods in public decision-making, Marika Karras and Kari Ella Read have in their master’s thesis estimated the economic value of ecosystem services and incorporated these values in a cost-benefit analysis (CBA).

The CBA was performed on measures preventing floods in urban areas through solutions called Sustainable Drainage System, or Open Stormwater Solutions. These systems are parts of a climate change adaption strategy, which mimic the flow control found in nature. The cost-aspect of the CBA was based on the investment and operative cost of these solutions. The benefit aspect constituted of the economic value of ecosystem services that these systems provide. The services include flood risk reduction, water and air purification properties, noise reduction, local climate regulation and increase of recreational values.

The result of the study indicates that only by including the monetary value of the ecosystem services in the CBA, the sustainable drainage system could be justified economically. This demonstrates the urgency of communicating environmental goods in monetary terms, and exemplifies how these goods can be made more visible in public decision-making.

ABSTRACT. The placement of wind turbines (WTs) in a wind farm is crucial to avoid wake shadow in order to fully exploit the wind energy and reduce power losses. In order to enhance the cost-competitiveness of wind energy as compared to other renewable energy sources or fossil fuels, it is imperative to reduce wake losses inside the wind farm to fully utilise the wind energy. Among the different approaches, optimising WTs placement within a wind farm layout is one of the most effective tools to achieve this objective, which however, is heavily dependent on the condition of local wind resources. This paper aims to study the optimization of wind farm layout considering the uncertainty of wind conditions due to its unpredictability and randomness. The effect of uncertain wind condition on wind farm optimization is investigated by robust optimization of wind farm layout with the description of wind speed pattern represented by Weibull distribution, while evaluation of wind farm power output under Weibull distribution by using Monte Carlo Simulation. The robustness of the wind farm layout optimization is evaluated by evaluating the wind farm expected total power output and the power output variation for the sampled wind speed and wind direction obtained by Monte Carlo simulation. Through robust optimization, it is found that under the fixed wind farm boundary and Weibull distribution, the optimally designed wind farm layout is very sensitive to incoming wind directions. It shows a very distinctive distribution of wind turbine placement even when the probability of wind directions is unchanged.

ABSTRACT. Companies are encountering competitive challenges, effectiveness in competitiveness requires firms to be efficiently responsive to current markets while effectively preparing for new markets and opportunities. In this circumstance, innovation plays a critical role to heighten competitiveness capacity of a firm. Therefore, firms which effectively manage to consider the demands of not only today but also future users and consumers through making well-structed and comprehensive innovation strategies will have more chance of survival. But the question is “how firms’ leaders can ensure that their firms are innovative, and they are taking the appropriate path which ends up with being uniquely innovative?” One way is to pay attention to innovation measurement. Innovation measurement can contribute to a significantly better understanding of innovation when conducted properly. Although the measurement of innovation is challenging due to its intangibility, it is very important for the development of innovation and therefore the future success of an organization. The main purpose of this paper is finding the factors that should be measured and improved by managers to make sure that their organizations are innovative and moving towards the right path. In this paper, these factors will be identified and integrated into a holistic conceptual framework regarding four Cs so called by the author “innovation capacity, innovation capability, innovation competence and consequences of innovation”.

ABSTRACT. Open innovation has received increasingly attention in scientific research (Chesbrough, 2002; Vrande et al., 2009). It showed to be a fruitful strategy to employ in highly dynamic and changing environment (Chesbrough, 2003) Since its emergence, evidence to support open innovation model was taken mainly from the so-called ‘high technology’ industries, such as computers, information technology, and pharmaceuticals. However, more resent work Chesbrough and Growther, (2006) confirmed the relevance of this approach in more mature industries. However, little is known so far under which conditions open innovation serves as appropriate strategy in such mature industries. This paper investigates innovation process in shipbuilding industry, which is characterised by long term trust-based relationships between involved partners and substantial financial investment. Using example from shipbuilding company in Norway we show how the innovative action of the firm may depend on the combined influence of entrepreneurial orientation (Lumpkin and Dess, 19996; Rauch et al., 2009) within the firm and cooperative links towards knowledge providers. We investigate innovation process that led to construction of a ship that was specifically designed to operate under the harsh arctic conditions. Findings indicate that open innovation approach is particularly suitable for innovation process in mature industries when product requirements are dictated by extra demanding conditions. We contribute to the domain of open innovation by specifying that firm entrepreneurial orientation, collaborative links with customer and partner are particularly suitable when the level of uncertainty is high.

ABSTRACT. The literature about science/ techno- parks has grown exponentially in recent years, thus, becoming a strategic topic within innovation management, industrial policy and science and technology studies. Despite this growing research, less attention has been paid to the environmental and societal benefits of science parks in their hosting regions. To contribute to this gap in the knowledge, this article addresses the research question: How can science parks contribute to eco-innovation processes in a region? The empirical evidence is gathered through a case study, Science park City of Knowledge, within the Panama Canal Watershed. The case presents an institutional eco-innovation, where different agencies provide services and products to secure sustainable water production. Based in an inductive analysis of interviews with key actors, document analysis and participant observer, we propose a model of science-park intermediation in the eco-innovation development process. In our model, we propose three phases for this type of process to unfold: a first intermediation process which we call network arena between local and global actors, characterized by intermediary functions as broad networking, scanning and information processing. A second phase of knowledge management is when the science park becomes a hub for knowledge-generating organizations including linkages with universities. The key functions in this phase are knowledge processing, generation and combination. A third phase implies capacity building and continuous learning, where the science park contributes by improving the institutional innovation through functions as testing and validation. Our results challenge the existing literature about science parks with a narrow focus on economic spillover effects on regions, or as hubs for attracting and developing cutting-edge technological innovations. We contribute to the literature on eco-innovation by developing a framework of institutional innovation at regional scale mediated by a science park.

ABSTRACT. Present paper suggest a theoretical model that describes the process of the development of responsible innovations on the firm level in health and welfare sectors. There is a need to develop new firm strategies in these sectors. This paper suggests to look on the concept of responsible innovation that was originally developed on the social level and to apply this new concept to the new area of firm strategy. The rapid global diffusion of information and communication technologies has greatly improved access to knowledge. At the same time, communication is cheap, information is a commodity, and global trade increases technological diffusion. As a result, firms and users, including those outside of industrialized nations, get early exposure to the latest technologies and information. General-purpose technologies such as mobile phones and 3D printers enable individuals to solve local needs and customize products. The combined effect of these changes is having a profound impact on the innovation landscape. Meanwhile, the healthcare sector is facing unprecedented challenges, which are magnified by budgetary constraints, an aging population and the desire to provide care for all. On the other hand, patients themselves are changing. They are savvier about their diseases, they expect their relation with the healthcare professionals to be open and interactive, but above all they want to be part of the decision process. All of this is a reflection of what is already happening in other industries where customers have access to large amount of information and became educated buyers. This article addresses the question of how ICT research and innovation may contribute to developing solutions to grand societal challenges in a responsible way. A broad definition of the concept of responsibility in the context of innovation is adopted in this paper. Responsibility is thus seen as a collective, uncertain and future-oriented activity. This opens the questions of how responsibilities are perceived and distributed and how innovation and science can be governed and stewarded towards socially desirable and acceptable ends. This article addresses a central question confronting politicians, business leaders, and regional planners

ABSTRACT. Intellectual capital management is a key factor in the transition to the digital economy. The rapid development of the IoT, cloud computing, Big data and other technologies leads to the creation of a complex interdisciplinary environment - industry 4.0. However, at the moment, the role of intellectual capital is not defined in this context. The purpose of this study is to analyze the existing work in the field of intellectual capital and the digital economy, to identify and systematize the main research problems affected by the authors, and to identify the most promising research problems. The study consisted of two stages: the search for literature, aimed at identifying the initial set of questions; structured classification of questions and research problems. We conducted a structured review of the literature, as well as the selection of articles on the selected criteria. The resulting set of articles was carefully analyzed, extracting potential research problems. Metaplan approach was selected for the classification of research problems. The result of the study is a list of 17 issues, which had been conditionally divided into 3 groups. It was also revealed that there is no single methodology for the formation and evaluation of intellectual capital in the digital economy at the moment. The analysis allowed to determine the further directions of research on IC in the digital economy, namely the creation of a unified methodology for assessing infrastructures, as well as studying the structural components of intellectual capital. The development of a new approach and methodology will theoretically generalize the emerging problems of the capitalization of intellectual activity and formulate them in terms of a scientific discipline. Studying the structural components of intellectual capital will contribute to the further development of the stakeholder theory and the search for new ways of interaction of all stakeholders in the formation of a knowledge-based economy.

ABSTRACT. Asset integrity-related decisions are largely qualitative and experience-driven. Mostly, Preventive maintenance strategies are dominating in oil and gas asset management. Recent changes in the oil an gas industry shows huge interest in digitalization and employing new technologies to find new benchmarked strategies. This has led many organization to adopt intelligent methods for smarter business and asset. Apply Sørco's Predict anc Prevent (PnP) methodology focuses on identifying equipment current (As-is) condition, combined with equipment history to precisely predict upcoming failures. The results provide useful input for planning maintenance, major modifications, overhauling and requirement for spare parts. The methodology is developed employing engineering expertise with reliability prediction decision making tool. Results from business cases reveal important information that provide useful insights in smarter asset integrity decisions.

ABSTRACT. The quality requirements of industrial water are not always as strict as those of water for human consumption, but failure to fulfil the particular needs of indus-trial processes may result in extremely high economic impacts. Águas de Santo André (AdSA) is a Portuguese water utility that must fulfil the needs of the Indus-trial and Logistics Zone of Sines (ILZS) within a framework of economic, finan-cial, technical, social and environmental sustainability. This paper uses the indus-trial water supply system of ILZS as a case study to test the application of a pro-posed value-based decision-making process for asset intensive organizations. The proposed decision-making process highlights the benefits delivered by competing alternatives for renewing the infrastructure. It contributes to higher engagement in value-based thinking over the lifecycle of the asset base of AdSA, enabling the balancing of performance, cost and risk, and an increased alignment with corpo-rate objectives and stakeholder needs and expectations.

ABSTRACT. Currently, the government is predominantly responsible for providing upstream water infrastructure in South Africa, thus, public administration and the associated institutions significantly impose certain engineering and financial realities on the corresponding capital investment decisions. This paper reviews theory on capital infrastructure investments and examines some of the complexities imposed by legislative/regulatory provisions and policy interventions. A situational analysis indicates that there is a concerning trend in the backlog of water infrastructure investments in the case study semi-arid country which is concurrently experiencing rapid urbanization and socio-economic transformation

ABSTRACT. The amount of different data available for maintenance decision makers is extensive. However, in practice most companies are not exploiting the data in the best possible way but are wasting their resources in sub-optimal data management processes. The objective of the paper is to review the current literature on exploiting data in maintenance decision making and to analyse how the principles of lean management could contribute to the issue of wasted resources. This paper describes the creation of a literature-based framework, which will be used as the starting point for empirical research and value modelling in the later stages of the study. The presented framework highlights the role of data in maintenance management decision-making situations, and suggests how the principles of lean management could be adopted in the process of managing maintenance data to improve its value and resource efficiency. The results of this paper will contribute to future research which will include modelling and optimizing the use of data in maintenance decision making.

ABSTRACT. Decision-making process in organizations is often a multi-actor problem. Studies to learn decision-making with a serious game have been previously done. The term “serious game” describes an intention of the player or the developer of the game to include a purpose other than pure entertainment into the game. Serious games communicate their purpose to the player through immersive and fun gameplay. While being engaged to gameplay, the players are knowingly or sub-consciously more receptive to learning and skill acquisition. The number of arti-cles about serious games in decision-making has increased during recent years. In this study, a systematic literature review is performed by using the Scopus da-tabase. The purpose of the paper is to categorize and analyse content of existing literature of serious games for decision-making processes in organizations. The paper also raises some points on what the current games lack considering organi-zational and technological trends.

ABSTRACT. Equipment management is gradually becoming more decentralized, and in many cases, the equipment owner, operator, maintainer and inspector are not the same legal entity. This slows down equipment data transmission between stakeholders and reduces business and technical process automation. In this paper we dis-cussed the use of a distributed ledger concept and propose to use private block-chain together with smart contract to resolve these challenges and to create a more automated and surveillance-free equipment lifecycle management process.

ABSTRACT. Building energy plants generate, consume and transfer a large amount of en-ergy to deliver various services, such as heating, cooling, lighting and elec-tricity. In large buildings, these energy plants can be complex to manage op-timally. Decision support systems are useful tools for aiding managers, how-ever, they too can also become overly complex. Participatory approaches for decision support system development and application are suggested in litera-ture for overcoming this issue. This paper presents the foundations of a wider research project that is applying participatory techniques in the development of a decision support system at a hospital energy plant. A generic integrated building energy plant optimisation model is formulated and expressed using problem domain language with the aim of promoting participation from stake-holders, such as facility managers or maintenance personnel, that do not nec-essarily have modelling expertise. While the formulation is targeted towards modelling the plant’s operational behaviour and decisions, the paper de-scribes how it can be used in a decision support system for aiding short-, me-dium- and long-term decisions of facility managers – highlighting again the model’s flexibility to meet stakeholder requirements elicited using participa-tory techniques. The project’s case study site, Lady Cilento Children’s Hospi-tal in Queensland, Australia, is introduced along with the planned methodolo-gy for participatory development of the decision support system. Finally, fu-ture directions are proposed for both further research and practical applica-tions of the contributions.

ABSTRACT. Mineral commodity prices have declined in recent years. This decline forces mining companies to find effective cost management strategies to sustain their operations. Otherwise, many operations will be ceased or suspended. Effective equipment utilization has strong potential to reduce operational costs. Furthermore, unexpected events or failures during the operation may not be properly considered in production scheduling. This affects the subsequent production process and causes operational delays; hence, the operation cost increases. Equipment condition is a key element to reach the desired production rate. This paper focuses on optimization of reliability parameters to improve the performance of the machine associated with its condition and calculation of drill bit consumption accordingly. Multiple input factors, such as operating parameters, operation time and maintenance time, were considered and controlled simultaneously to simulate drilling operation by stochastic modeling technique, using historical data. A case study was carried out using discrete event simulation (DES). Multiple simulations were used to quantify risk. The research outcomes show that the proposed approach can be used as a tool to assist production scheduling and asset management.

ABSTRACT. In this study a computer simulation model was built for making predictions about the fleet availability and spare part stock development concerning the whole life span of the fleet. The computer algorithm included algorithmically the corporation fleet running, repair, transport and storage rules. All event durations have been assumed to be stochastic that is random but evolving from the specific distribution. Failure probability distribution functions for the simulation were generated from the historical data of the components of the systems of the fleet. As a contribution, this study introduces a method of estimating failure probability density function for each failure count individually. This approach makes possible to capture the effect of actual repair process to the probability of the next component failure in simulation.

ABSTRACT. In this paper we consider the situation where the OEM of a repairable system wants to estimate the mean time to failure (MTTF) or lifetime distribution of a given component of the system so as to forecast the demand of spare parts in a given time interval or to optimize the maintenance policy of the component. The OEM does not have field failure data of the component but has the installed base information of the systems and the spare parts consumption information of the component. Due to lack of field failure data, the failure-time-based approach is no longer applicable. To overcome this difficulty, we propose a novel approach to es-timate MTTF of the component based on spare parts consumption data. The pro-posed approach is based on the assumption of Weibull renewal process and a modification for the asymptotic renewal function, and provides an accurate esti-mate of MTTF with a relative error of smaller than 2.1%. In addition, it is found that MTTF obtained under the exponential distribution assumption is a consider-able overestimate when the observed time interval is relatively short. One real-world example is included to illustrate the appropriateness and usefulness of the proposed approach.

ABSTRACT. Companies plan investments and acquire assets evaluating among different alternatives what is more suitable to satisfy their needs. Management choices should always be forward-looking, accurate, and coherent; otherwise, firm competitiveness in the market may be profoundly affected, or, even worse, its survival may be severely put to the test. In this context, implementing a robust and refined decision-making process is essential. Total Cost of Ownership (TCO) is a reliable approach that can be used to estimate the convenience of an investment in the long-term, since it takes into account all costs associated with the entire lifecycle of an asset. The concept of TCO is nowadays well known and widely studied in the literature, and can be used for several purposes, not exclusively related to economic benefits of a managerial decision. Notably, it can be used as a decision making parameter to be evaluated in the selection of the kind of Product-Service System (PSS, e.g. maintenance, training) the customer could buy from a supplier, even if benefits of using TCO are undiscussed and not deeply validated. In particular, a logical process to compute TCO from suppliers perspective is proposed. This would allow supplier to propose to their customer the most appropriate and convenience PSS, as a combination of the asset and a set of services to guarantee to customers cost and operational efficiency in the long-term. The development process is validated by using real data related to alternative PSS scenarios a supplier can propose to the customer.

ABSTRACT. This paper reviews theory on the replacement of assets and examines the application of the theoretical models for practical decisions to replace engineered assets deployed for mining operations. The study considers that asset replacement decisions are a part of a broader value-driven ethos and thus reveals that practical decisions to replace engineered assets deviate significantly from the theoretical models. Although return on investment, net present value, and internal rate of return are widely acknowledged theoretical approaches towards capital replacement decisions, however, the respondents mostly rely on the experience of maintenance personnel and fixed service life methods when deciding whether to replace an operational asset. Interestingly, the availability of funding, technical support from the asset manufacturer, supplier or vendor, and technological advancements were highlighted as having the greatest influence on replacement decisions in practice.

ABSTRACT. Issues related to supply chain finance, also known as supplier finance or reverse factoring, have been in focus of both academics and practitioners after the finan-cial crisis. The focus of schemes in this area is to support the working capital management of the key suppliers of buyer. The schemes are implemented be-tween tier-1 supplier and buyer. In this paper, we study working capital manage-ment in an information and communications technology business ecosystem pro-ducing desktop computers, spreading the study from the focal actor to the com-ponent suppliers in the furthest tier. We show the potential that exists to release capital from operational issues to more strategic purposes.

ABSTRACT. Nowadays, optimal supplier selection process is essential for the performance success of business organizations. Total cost of ownership (TCO) is a method which enables purchasing firms to access all key information for assessing and selecting suppliers. An effective supply chain process which is based on TCO tool is argued to extend purchasing decisions beyond the initial price and consider the long-term perspective of all hidden and explicit costs for carrying out business with various suppliers. All purchasing enterprises are seeking the maximum profit from the purchased products/services. In order to identify all cost drivers, both quantitative and qualitative methodological approaches have been operated and analysed in the TCO application. However, it is not clear which methodologies are better, and under what circumstances, in relation to the TCO concept. This paper examines the application of a range of qualitative and quantitative methodologies in TCO purchasing techniques. The paper outlines and reviews the strengths, limitations and barriers of both qualitative and quantitative methods. In conclusion, the framework supports optimizing decision-making based on total cost of ownership in a firm’s cost management process.

ABSTRACT. Political institutions have challenged government agencies in Norway to reduce administrative costs and optimize its life-cycle management since public sector accumulated an alarming maintenance backlog rate systematically over many years. The government in Oslo has applied an order & rent model, where a public agency orders the construction of a building, and later rents the building based on the initial investment-cost and estimated life-cycle costs. The municipality has at-tempted to solve lack of maintenance-funds by increasing the rent with realistic capital- and operating funds, and a consumer price index that regulates the rent annually. With regard to life-cycle costs for building-portfolios, information-sharing processes have with the new trend industry 4.0, serious potential to in-crease efficient file sharing between agencies within the public government. A cloud sharing process will streamline cost-management within the governmental body to increase cost transparency. This paper provides examples how to im-prove the life-cycle management process in governments, how to process-control the cost, track the life-cycle cost and manage the reporting to political institutions.

ABSTRACT. An early accurate prediction of remaining useful life (RUL) is essential for improv-ing the machine reliability and prevents system failure. This study proposes an ef-ficient technique to evaluate the health state of bearings and estimate the RUL. It employs the Exponential Moving Average (EMA) crossover technique to actively anticipates an upcoming failure trend of the bearing and the support vector regres-sion (SVR) to constantly predict the RUL of the bearing while its health state is still within the EMA crossover threshold. Once the health state of the bearing exceeds the EMA crossover threshold, Kernel Regression (KR) technique along with SVR will be utilized to instantly predict the failure point and estimate the RUL of the bearing. The effectiveness of the model is validated by experimental data collected from the Center for Intelligent Maintenance Systems (IMS). The proposed prognos-tic technique shows an effective early failure prediction with great accuracy in com-parison to the common model.

ABSTRACT. This paper presents a fault diagnosis technique for rolling element bearings based on a combined de-trended fluctuation analysis (DFA) and variational mode decomposition (VMD). DFA is used first to filter out the uncorrelated trends in a non-stationary time domain signal acquired from bearing condition monitoring. The analysis can reveal the long range correlation existed within the data series, and is used to determine the number of modes K for VMD decomposition. The VMD then decomposes the CM signal into K band-limited intrinsic mode functions (BLIMFs) and the fault relevant BLIMFs are selected based on DFA for the re-construction of the filtered signal. The result shows that the de-noised signal after VMD decomposition can detect an incipient bearing defect from a low signal-to-noise ratio (SNR) condition monitoring data.

ABSTRACT. An adaptive multivariate process modelling approach is developed to improve the accuracy of traditional canonical variate analysis (CVA) in predicting the performance of industrial rotating machines under faulty operating conditions. An adaptive forgetting factor is adopted to update the covariance and cross-covariance matrices of past and future measurements. The forgetting factor is adjusted according to the Euclidean norm of the residual between the predicted model outputs and the actual measurements. The approach was evaluated using condition monitoring data obtained from an operational industrial gas compressor. The results show that the proposed method can be effectively used to predict the performance of industrial rotating machines under faulty operating conditions.

ABSTRACT. Demand mode is often used to describe safety instrumented functions whose functionality is only performed on demand and demand rate is low. Many critical safety functions are designed as on-demand systems. Periodic tests and inspections are usually applied to reveal failures. Current studies on the systems’ reliability are focused on retrospective failures instead of future failure modes. Prognostics studies of on-demand systems face many challenges, among which is the inability of predicting future states of the systems. The study uses a data-driven approach to implement prognostics and health management (PHM) tasks on systems with demand modes. The paper examines how periodic tests influences system reliability and how test interval could be evaluated and defined. The application of PHM to on-demand systems can help enhance company’s confidence in system reliability and develop predictive maintenance practices. The approach can also be used as a practical tool for supporting decisions related to extension of shortening of test intervals.

ABSTRACT. Complex utilities such as power distribution, nuclear reactors or large water networks employ multiple data sources to provide key information regarding asset performance and condition. These include operational logs and fault tracking, SCADA real-time output, work and financial transactions, and a wide variety of formats of inspection and condition monitoring data. Traditionally these data sources serve different parts of the organisation and are rarely integrated to provide a holistic view of the current operational capability of the asset portfolio.

To integrate this data requires a complex view of the assets, linking network or functional connectivity with regional locations to build up an understanding of how the extensive fleet of assets must work with each other to achieve the operational objectives. This view needs to consider the variety of asset types, each with their own specifics for attribute data which must be utilised in an age-based appreciation of where assets lie with respect to through life consumption of condition and deterioration from as-designed performance.

This paper reports progress in a rules-based approach to handling data from a range of utilities, where the rules form part of the design of systems to handle the multiple data stream and complex configuration. Such systems determine current and future risk profiles associated with condition and performance. The ensuing reports identify sources of high risk across thousands of individual assets in multiple systems, plus recommend appropriate windows in which work may be scheduled to address the risk. Using rules, the approach is designed to be agnostic as to type of utility or its operational purpose.

The assessment of future risk is subject to scenario testing for different utilisation profiles plus the benefit of change-out of major assets. A system may have its overall life extended with judicious investment of key components which have low reliability and the failures stress the overall system. Hence the outcomes of this work include both short-term responses for immediate repairs as well as longer term planning for capital renewal.

ABSTRACT. Asset management is an enabling discipline that is defined by the International Standard ISO 55000 as the coordinated activities of an organization to realize value from assets. Prognostic and health management (PHM), component and system reliability, risk assessment, asset management planning and organizational structure are all part of the asset health management process. Those activities are themselves specific disciplines which have their own standards, goals, expert communities and visions. However links between them as a whole process still have to be clearly understood and defined. The aim of this paper is to propose a holistic asset health management model depicting interrelations between activities. The well-known Data-Information-Knowledge-Wisdom (DIKW) hierarchy is applied to the asset management process to shed light on interactions between key activities and their contributions to the asset management value chain. To illustrate the concept, an exam-ple is shown in the context of hydropower generation.

ABSTRACT. This paper presents a probabilistic analysis of a compilation of test data on the fatigue endurance of Aluminum Conductor Steel Reinforced (ACSR) cable–clamp systems. A brief review of the testing and measurement methods used to perform fatigue tests on conductors are described. Theoretical arguments based on the properties of extreme value distributions and random vibrations are presented which indicate that a Weibull S – N model is the most appropriate among models previously proposed in the literature for fatigue of ACSR cable – clamp systems. Predictions from the model are presented in terms of idealized stresses using bending amplitudes. Statistical tests are performed to verify that the Weibull distribution provides a good fit to the conductor fretting fatigue data. Validation datasets independent of the training dataset are used to evaluate the predictive ability of the model. The proposed probabilistic model is shown to be a reliable means of predicting the residual life of conductors subjected to aeolian vibrations for transmission line management and conductor replacement planning.

ABSTRACT. Power grids are the backbone of most economy and greatly contribute to our quality of life. Throughout the world, the vast majority of powerlines were built between the 60’s to 80’s. Their aging and associated replacement programs are undoubtedly an upcoming challenge, which will require proper condition assessment and the definition of relevant end-of-life criteria. At Hydro-Québec, the above contexts motivated the launch of an integrated strategy aiming at providing the tools, technologies, and know-how to efficiently determine a health index of its conductors (worth about 30% of a transmission line reconstruction cost), without requiring heavily to the more accepted field sampling methods. The topic of the proposed paper is to explain the strategy that unfolds in three different projects, and which draws from IREQ - Hydro-Québec Research Institute’s specific expertise. At the core of the strategy are the different robotic platforms that were developed at IREQ to reach and travel along the conductors: a versatile and robust platform that can travel the power line while crossing the different obstacles on its way (LineScout), and a custom-designed drone that can safely land onto the conductors (LineDrone). Mounted on the former is a portfolio of Non-Destructive Testing (NDT) technologies, which was developed to match each of the three prevalent aging mechanisms: a portable X-ray scanner for the detection of under clamp/inner layer broken strands, an eddy current sensor for the measurement of the steel core zinc losses, and a magnetic flux head that assess the steel section loss due to corrosion, or local breaks. Secondly, conductor degradation and aging modeling project aims at determining the actual health index of the scanned conductors, and their remaining useful life (RUL). It is based on conductor’s type, field results, and environmental conditions. Finally, a large scale database tool will be developed and deployed to provide a seamless integration between the enterprise conductors’ installation and operation data, field generated data provided by NDT sensors, line technician key observations, and the output of the aging model. Vertical integration will thus be achieved in order to provide tools for supporting decision-making in asset management.

ABSTRACT. Hydroelectric turbine start-up protocols have a significant influence on the fatigue reliability of such asset. Fatigue cracking, is one of the two main degradation mechanisms and often considered the most difficult to predict. Using fatigue reliability model developed previously by the authors, we study the influence of changes in the start-up protocol. Two units are studied. For the first unit, two protocols are studied and for the second unit three protocols are considered. The goal of the study is to look at the expected monetary value (EMV) as a metric able to combine the expected probability of failure with the monetary consequence of such failure. Our results shows that such metric is suitable to identify which start-up protocol is the most advantageous and why?

ABSTRACT. This paper is about the latest developments to support risk management of assets at Elia, a Belgian transmission system operator who also has some distribution network operator tasks. The story of risks management within the company started more than 5 years ago, when a methodology was developed to assess the criticality for continuity of supply of individual asset. The output of this methodology is still used nowadays. As an example, maintenance policies do take into account the criticality of the asset on the network to define its maintenance frequency. So do replacement decisions. However, as many other TSO’s, Elia is also facing new challenges. As an example, there are strong constraints on investments and workforce available associated with uncertainties and scarceness of data. Risk management should help tackle these problems through providing support to prioritize actions and make sound and good decisions. In more concrete terms, there is a need for better addressing many diverse questions such as dealing with system redundancy, with the mutual impact of assets, with the impact of low voltage assets (protections) on high voltage assets, better assessing load at risk, asset end of life, the impact of a failure on several risk dimensions, the efficiency of risk mitigation measures, etc. In the frame of this paper, it was decided to focus on the solutions found to better assess the impact of protections on high voltage assets and the interaction between assets. To tackle these topics, one could think of implementing sophisticated methods such as probabilistic assessment, but this in turn requires asset reliability data, resources, skilled persons and lots of time for development, which are all sparse quantities. Therefore progress was made with pragmatic 80/20 methodologies, which though not perfect, allows delivering valuable information. The corresponding developments are discussed in the frame of this paper.

ABSTRACT. The Norwegian Public Roads Administration (NPRA) has recorded bridge element damages in a database for all the bridges it manages since the 1990s. This paper presents a comparison of three methods to establish element condition based on damage records. The methods consist in a non-parametric procedure based on the worst damage registered in the element, linear regression considering also bridge and road characteristics data and classification through an artificial neural network. The methods are assessed using a set of 159 bridges inspected in 2016.

ABSTRACT. An oleo-pneumatic shock absorber of a fighter aircraft loses some of its nitrogen charge and oil fill over time, due to leakage through seals. Usually, only gas can be added to shop absorber in line maintenance. This creates an imbalance to the ratio between gas and oil, and affects both the damping and the stiffness of the shock absorber. Measuring the gas and liquid content inside a shock absorber is infeasible. Thus, other variables that can act as indicators of an imbalance, must be identified. Field measurements are expensive and time consuming. Therefore, a coupled model of a fighter aircraft landing gear and its shock absorber is created. Using the model, landings with different sink speeds and aircraft masses has been simulated. Simulations with a standard, and later imbalanced, gas-liquid ratio are conducted. Results from these simulations are discussed and presented here, and variables that can be used as indicators of this imbalance are identified.

ABSTRACT. Costs prediction throughout the life cycle of building projects is supported by the life-cycle-cost (LCC) concept, encouraged by several international and regional standards (e.g. ISO 15686-5, EN 15643-4, EN 16627), procurement guidelines and regulations (e.g. the European Directive 2014/24/EU). However, public procurers of building projects still face difficulties regarding the costs estimation and time ratios of systems and components over the life-cycle of building facilities. Public databases with the adequate quantity and quality of economic information is needed. The existing ones often present problems such as those of inadequate data granularity, incompleteness, inaccuracy or data structures with formats that make comparison and extrapolation difficult. This paper addresses these problems and proposes a standardized structure for economic data collection throughout the whole life-cycle of building as well as economic life-cycle indicators. A case study related to 160 public school buildings constructed in Portugal since the 1940s is presented as to show the potential application of the proposed structure towards widespread use of the LCC concept in public procurement environments.

ABSTRACT. There are huge reserves of hydrocarbons in the Arctic region. However, owing to the harsh climate, darkness, problems with communication, long distances, lack of infrastructure and other challenges, offshore operations related to hydrocarbon extraction are more difficult and more expensive. Nowadays, gas extracted from the Arctic shelf is transported mainly by pipelines and liquid natural gas (LNG) carriers. Both methods are quite expensive. In this paper we consider the application of innovative compressed natural gas (CNG) technology to transport gas extracted from the hydrocarbon fields in the Arctic. The technology is not widely commercially used yet. The calculations show that the gas transportation costs using CNG carriers are lower than those using LNG carriers and pipelines for smaller gas fields and distances from the source to the consumer up to 4000 km. The paper would be interesting for practitioners from the oil and gas and shipbuilding industry, policymakers and scholars.

ABSTRACT. Availability of offshore systems immediately affects operational costs and is therefore an important performance indicator. In order to ensure high system availabilities, failures of equipment must be avoided. This is especially important for offshore equipment due to slow spare part supply and high repair costs at the usually remote deployment locations on the open sea. A detection and thus prevention of failures can be achieved by applying condition monitoring systems on relevant system components. Since condition monitoring usually requires elaborate and therefore costly sensor infrastructure, this paper presents a method that aims to acquire condition information of gearbox components of ship-mounted offshore crane winches by using only already available and easily accessible external load information, such as motor torque, wave height and carried payload. Since the condition of machine elements depends on the experienced load history, simulation models are built up in order to create transfer functions, which determine the local component loads based on the external loads. Especially for offshore cranes typically equipped with Active Heave Compensation (AHC) systems, the drivetrain loads depend highly on the wave-induced motion of the ship and subsequently of the crane. To be able to take these influences through transfer functions into account as well, a ship dynamics model is implemented to predict the crane motion based on the wave motion and basic ship dimensions. With the local component loads available, each component’s health status based on fatigue can be estimated by applying component lifetime models. In combination with the system reliability structure of the entire winch drivetrain, the overall system failure probability can be derived from the estimated components’ failure distributions. Furthermore, critical components can be identified, monitored and replaced in time to avoid unplanned downtime of the equipment.

ABSTRACT. Digitalization and big data has led to health indices becoming popular for representing the overall condition and ability of an asset to perform its function, and for assisting in lifecycle decision-making. Health indices have been applied successfully in various asset intensive industries, such as the electricity distribution, oil and gas and build industries.

Limited research is available about health indices for passenger rail rolling stock. Previous research focussed on either developing health indices for individual rail rolling stock components or sub-systems, or developing integrated vehicle health management and prognostic health management systems, which focus on detection, diagnosis and prognosis in combination with maintenance strategies. The life of rail rolling stock can be affected by various factors such as maintenance strategies, driver behaviour, environmental conditions, load conditions, commuter behaviour and other external factors such as the effects from the wheel-rail and pantograph-contact wire interfaces. However, existing research does not elaborate about combining the important life cycle factors of the rail rolling stock into an integrated health index.

This paper shows the need for developing an health index for passenger rail rolling stock, to assist with decision-making about the maintenance, refurbishment and replacement of the these assets across its life cycles. The research presents an approach to develop an integrated health index for rail rolling stock. Based on a comprehensive literature review of the latest research about health indices, the building blocks for developing such an index are identified. Big data related challenges and strategies for identifying appropriate data from information systems for developing an health index are further addressed by the research. Finally key performance indicators for rail rolling stock are identified and these indicators are integrated into a conceptual health index. The paper concludes with recommendations about the practical implementation of health indices for rolling stock in the rail industry.

ABSTRACT. The social housing sector is submitted to increasing challenges, related to the aging of this sector, from poor quality, degraded life environment, high running expenses, tenants’ low income; as well as reduction of the public funding for this sector and increasing sustainability requirement to reduce both energy consumption and greenhouse gas emission. To meet these challenges this sector has to innovate both management and operation methods in order to optimization effectiveness in this sector together with the improvement of its performances. This strategy should be based on smart management. This paper proposes a smart life cycle management of social housing assets base on the result of the technology innovation in this sector. It contains both the original and the real-time information of assets, in order to build a smart- knowledge strategy for whole life cycle of asset management to improve the social housing efficiency and quality. The paper presents a discussion of this smart life - cycle management system. It proposes the methodology for each process of this system's strategy. Its goal is tracking the optimal management of the social housing asset.

ABSTRACT. Uncertainties are a key issue when planning for long term capital investments in asset intensive organizations. Amongst the sources of uncertainty are the quality and accuracy of readily available data and the assumptions underlying the various estimations involved in capital renewal plans, namely those related to re-placement costs and useful lives of the asset base. This paper addresses the effect of such uncertainties in the capital investment strategies for the industrial water supply system of the Industrial and Logistics Zone of Sines (ILZS), installed in the Portuguese seacoast around 40 years ago. The discussion includes deterministic and stochastic approaches to an asset management KPI that is presently being tested in the Portuguese water sector - the Infrastructure Value Index (IVI).

ABSTRACT. Abstract Benchmarking is widely applied in industry for comparing own prac-tices and processes with peers and for learning of the practices in relevant sectors. Companies utilize benchmarking to find out development targets, and to benefit from existing good practices. In capital-intensive industries, the production sites differ from each other and it may be difficult to find a valid reference group for any comparisions. Thus, when considering benchmarking, the first step is to rec-ognize relevant reference groups, which best can learn from each other. Machinery manufacturing companies have often a wide installed base com-posed of delivered products. This fleet also provides possibilities for data collec-tion and service development. Service providers could exploit benchmarking ap-proaches together with their customers when looking for development needs in the asset management practices, and for developing service portfolio options. In this paper, we present a systematic benchmarking framework for identifying service opportunities, and for optimizing the offered service solutions. The special emphasis is in the metals processing industry. The developed benchmarking method and tool helps to compare different sites according to their operational and maintenance characteristics and to identify potential sources of value.

ABSTRACT. The participation of multiple stakeholders in the innovation process is one of the assumptions of Responsible Innovation (RI). This partnership aims to broaden visions, generating debate and engagement. Under this premise, the present study sought, based on a meta-synthesis, to evaluate how the stakeholder participation in RI takes place. For that, were identified qualitative case studies that studied the participation of stakeholders in processes of responsible innovation. Those studies have shown that, although participation is achieved when innovation is already in the process of being implemented or already inserted in the market, it serves as a basis for modifications, both in the developed product and in the paradigm of innovation. Based on the concept of Responsible Innovation and its dimensions, the role of stakeholders in the context of innovation is restricted to substantively motivated participation, for example where political choices can be legitimately coproduced with audiences, incorporating an authentic diversity of knowledge, values, and social meanings in a substantive way. The agents that stimulate their participation are academic researchers and researchers linked to multi-institutional projects. It is noticed that the studies favor the participation of multiple stakeholders. Policymakers (including funding agencies, regulators and executives), business / industry representatives (internal or outsourced innovation departments and / or some R & D base); (such as foundations, associations, social movements, community organizations, charities, media), as well as researchers and innovators (affiliates of various institutions and organizations at different levels). One point that stands out is the change of vision of one stakeholder over the other. Although it is pointed out the difficulty in the dialogue, it is possible, by inserting them collectively in the discussion, that the different stakeholders develop a better understanding of the different points of view.

ABSTRACT. In the twenty first century, more than any other time in the firm’s history, companies are under the global competitive pressures in a context that is widely dynamic and consequently makes them encounter a huge number of unpredictable changes. Leaders of firms, on one hand face the immediate pressures of delivering value to increasingly sophisticated and globally diverse customers while accelerating the return on these efforts for financial stakeholders and on the other hand, strategic leaders must identify and prepare for disruptive technologies and emerging market opportunities over the long-term. Therefore, combining two different innovation strategies, exploration and exploitation is a huge challenge for managers and leaders of the companies. The aim of this paper is to find different perspectives for combining these two strategies in companies. The literature review shows that this phenomenon forms a new type of organizations so called ambidextrous organizations. Therefore, this paper’s main focus is on this sort of organizations, various studies and research around this concept as well as recommended strategies for resolving the associated conflicts. It is discussed that there are distinct logics behind this concept which each has a very influential effect on the next decisions and behaviors of organizations. In this paper, based on the literature review, eight perspectives are extracted and described and at the end a comparison of different strategies regarding their logic and main focus has been presented.

ABSTRACT. Low-voltage local electricity intelligent management is an essential portion of smart grid research. Thereinto, a precise prediction of domestic energy consumption is a pivot in establishing household / neighbourhood energy management system to achieve local smart solutions including consumption auto-balancing, micro generation and storage system, and neighbourhood energy sharing. Recent years, a large amount of literature has evolved on the use of artificial neural networks (ANNs) for electric load forecasting. Various ANN topologies and training methods are employed and discussed. However, the solutions are generally developed as case by case studies. The advised network configuration for each specific problem is commonly selected through empirical or enumerative approaches. Furthermore, the inherent defects of neural networks like local optimum are also necessary to be considered conscientiously. In this paper, a novel evolutionary ensemble approach is presented to pool and select proper neural network topologies and training methods to forecast domestic energy consumption efficiently. The approach utilizes an evolutionary method to select and multiply better performed network individuals in a network pool to optimize prediction quality. Forecast results demonstrate that the approach achieves a more accurate energy consumption prediction comparing with commonly utilized neural network configurations in a feasible time duration. At the same time, the proposed approach conduces to bypass the ANN local optimum problem.

ABSTRACT. In many industrial sectors we have already begun to see how new business ecosystems gradually emerge turning the traditional business practices upside down. Particularly within relatively conventional sectors, such as upstream oil and gas (O&G) and land-based process industry, we expect to see substantial changes in the years to come. The change is partly driven by the modern technology (r)evolution and partly by new operational scenarios. These trends will have a major long-term impact within asset engineering and management due to growing demands for smarter, safer, and efficient asset portfolios. To succeed with this, salient features and capabilities of the core technology platform need to be considered, both within the IT domain (Office Network) and the OT domain (Process Control Network). This is both necessary and essential to succeed with a new philosophy of a modern connected business eco-systems within asset management.

In this paper, we give some analysis and reflections on challenges with current core technology platforms for the oil industry, based on previous experiences from various operation and maintenance improvement projects on the Norwegian Continental Shelf, as well as several topside new-build projects (O&G platforms). Thereafter, we perform a critical analysis and creative reflections on latest research and practices related to the use of core technology platforms towards creation of New generation business processes enabled by Industry 4.0. Lastly, two examples from Integrated Operations (IO) on the Norwegian Continental Shelf are given to show how the business ecosystem for recent industry could be changed, identifying critical features and elaborating on operational principles.

ABSTRACT. Australia’s warships and submarines are collectively the most complex, critical and expensive warfighting assets within Defence’s inventory. Asset managers make decisions where beneficial short-term effects may cause unforeseen long-term repercussions leading to increased life cycle costs, decreased (or lost) capability and reduced operational availability that affect the operations and maintenance profile across each usage and upkeep cycle. Predictive life cycle forecasting provides an objective and empirical method to quantify budgetary requirements based on estimated future effects to operational readiness and seaworthiness. The life cycle forecast is a key component of each vessel’s as-set management plan and records the operations and maintenance profile across the asset’s service life by establishing requirements for products and services needed to support the vessel within the prescribed asset management system. Predictive life cycle forecasting initially begins with establishing a baseline life cycle model that amalgamates contiguous operational running periods and scheduled maintenance activities across multiple usage and upkeep cycles to provide a time-phased representation that projects expected costs, operational availability and capability baselines from commissioning to disposal. Variable phases, states and modes provide the means to adjust model parameters to probabilistically characterise options available to asset managers when evaluating and assessing various scenario outcomes. An interactive model can pro-vide asset managers with immediate feedback based on options explored with-in the model. Using each vessel’s life cycle model, predictive life cycle fore-casting can provide a consistent and logical method for systematically updating asset management plans. Robust and comprehensive predictive life cycle fore-casting supports asset management decision-making to more accurately optimise warships’ and submarines’ availability, capability and affordability across the life cycle. As a fully scalable method, it can be applied to a single vessel, class of assets or to the collective fleet as a fundamental technique to support Fleet Life Cycle Management.

ABSTRACT. With an excess of 1100 road tunnels, Norway has the highest density of road tunnels in the world. Fire fighters are left with a great task, to save us from the inferno as tunnels get longer and deeper. Tactical decisions needs to be taken quickly, based on the available information, and robots has proven to be an effective tool to gain situational awareness to reduce risk and increase efficiency. This presentation will share key elements of how robots can empower humans in demanding applications.

ABSTRACT. Abstract: The Mont Blanc tunnel fire March 1999 killed 39 persons, of which most of them died within 15 minutes due to intoxication. In Norway there have been several fires the recent seven years. No single road-user has died from intoxi-cation in those fires, in spite of being engulfed with smoke for more than 1.5 hours. The tunnel safety discourse amongst tunnel owners and also researcher’s turns towards questioning whether current longitudinal ventilation strategies can be used to design the tunnel system to meet the self-rescue principle. The Norwe-gian Public Roads Administration could in this perspective reduce its effort to in-vest in safety measures ensuring safe havens for road users trapped in smoke and other fire preventive measures. We are very critical to such a development of tun-nel fire safety. This paper raises questions about predictability of smoke disper-sions in case of tunnel fires as well as human tolerability of toxic gases from fires. We conclude with issuing designs of research studies to increase gaps of knowledge revealed in the literature.

ABSTRACT. The use of traffic related data to detect risk situations and ways to intervene.

ABSTRACT. A strategy for cooperation in emergency management has been developed and po-litically agreed upon by the Rogaland County Council, Norway. This region con-sists of many different actors within societal safety and emergency management. The strategy aims at strengthening the existing cooperation, establishing profes-sional centres and further developing competencies in their emergency response ef-forts within the region. The region has more than twenty road tunnels either in the planning phase, under construction or in operation. The emergency services have established a new organisation of their cooperation to ensure coordination, learn-ing and supervision. This relates both to exercises and real event operations. An important tool in this respect is a recently developed handbook for cooperative ex-ercises. The book is used in planning, execution and follow-up of all cooperation exercises. In this paper we present our evaluation model for following up the co-operation exercise guidelines, with special attention to events in road tunnels. We employ a learning model that extends the notion of learning from observed chang-es to also include confirmation and comprehension of cooperation activities.

ABSTRACT. The Norwegian Tunnel Safety Cluster (NTSC) was incorporated into the Norwegian Innovation Clusters (NIC) programme in June 2016, and formally established in November of the same year. The cluster defines "tunnel safety" based on a holistic perspective that addresses both the prevention of unwanted incidents and the mitigation of consequences when road-, rail- and metro-related tunnel accidents occur.

The goal is to develop a sustainable and innovative cluster with the aim of commercialising solutions for improved tunnel safety in Norwegian and international markets. This will contribute towards safer tunnels and fewer accidents, which is vital to road users, the fire and rescue services, the transport sector and local communities everywhere.

The cluster has expanded vigorously since its incorporation into the NIC programme in June 2016. On the day of the application it comprised 69 participants, but now we have more than 100 companies, 8 R&D/academic and innovation centres, and 11 public organisations, including the emergency services.

ABSTRACT. REWITEC® is an independent, medium-sized business that develops an innovative nano- and micro-particle based additive technology. This technology uses lubricants for surface treatments in engines, gears and bearings in industry sectors like, WIND ENERGY, INDUSTRIAL, MARITIME & AUTOMOTIVE applications. The active components of the coating concentrates are in a constant upgrading process in close cooperation with science and practice for years, like, with the University of Giessen, the competence center of Tribology Mannheim - Germany and Sentient Science, a 3rd party, which is working with the Department of Defense and NASA and the wind power industry worldwide. The abrasion and the wear of tribological systems like bearings, gearboxes and combustion engines and similar units is a key issue when it comes to service life and sustainable functionality.

The latest presentable test results can be obtained from the test, over the wear development on a gear tooth over a period of 2 years. The gearbox of the wind turbine was after over 10 years of operation prophylactically treated against tribological wear such as micro pitting, and seizing of surfaces. The analysis was documented with the aid of surface imprints before and after the application of the selected tooth flanks, where operational wear and in the foot area seizing and stray metallic particle run through marks were visible. Another illustrative test is the FE-8 test. It’s used to examine lubricating oils and greases with regard to their wear and friction behaviour under lubricant and bearing-specific influences. To assess the suitability of the lubricant to be tested, the friction and temperature behaviour and the wear is determined in conjunction with the resulting weight loss of the bearings in the test arrangement. The tests also allow the creation of surface measurements and lubricants and reaction layer analyses.

Method: How REWITEC® works: 1. The REWITEC® silicone coating DuraGear® W100 is conveyed via a lubricant into the gearbox, bearing or engine and gets in this way to the stressed metal surface. 2. As a result of the crystalline temperatures that arise in live operation, the product’s coating particles react with the molecules of the metal surface and the chemical/physical process is set in motion. 3. On the basis of this chemical bonding, the rubbing metal surfaces gain a ceramic quality, producing a new, corrosion-resistant metal/ceramic surface. In the process, the material properties in relation to friction and wear improve appreciably, whereas the lubricant properties remain unchanged.

The wind turbine was inspected again. Prior to that time, the wind turbine was able to reach different load conditions. Then, a second and third imprint could be taken at the corresponding tooth flanks of the wind turbine. Treatment with REWITEC® after 4 weeks:

• Stray metallic particle run through and seizure are greatly smoothed out • Run through marks and pitting are greatly smoothed out

Results:

The Results after a treatment with REWITEC® after 2 years showed following: • Less stress for the tooth flank • Reduction of the surface roughness and friction force • Improved load carrying capacity The improved surface structure of the tooth flank and bearings should substantially increase the life of the gearbox system. The practical results confirm the scientific studies at the above-mentioned research-institutes. The result of the FE-8 test has additionally been positive. By the test once without REWITEC® and once with REWITEC® resulted the evaluation:  - Light run marks and smoother surface - 17% less wear with the REWITEC® treated lubricant

Also a test of Sentient Science with its DigitalClone® technology predicts that a Winergy 4410.2 gearbox treated with REWITEC® DuraGear® W100 has a significant improvement in life than untreated gearbox and representative turbine operating conditions. Specifically, for bearings, REWITEC’s DuraGear® W100 treatment is expected to improve the overall contact fatigue life by a factor of 3.3. For gears, REWITEC’s DuraGear® W100 treatment is expected to improve the overall fatigue life by a factor of 2.6.

Conclusion:

REWITEC® main applications are gears and bearings in wind turbines. Through the development of a special surface treatment concentrate based out of nano- and microparticles, REWITEC® focuses on the long-term running of engines and gearboxes and thus contributes a decisive impulse to sustainability and energy efficiency. Scientific studies demonstrate the effectiveness of REWITEC® products. Less friction and surface roughness in tribologic systems means:

• Less stress and wear for the gearbox and bearings • Higher efficiency • Less stress for the lubricant • Higher reliability and availability, no downtime • Cost savings • Longer Lifetime

ABSTRACT. Business models describe the way a company creates, delivers and assimilates value. Value can be offered as a physical product, a real or virtual service, or as the combination of products and services. Business models based on combined offers, such as the Integrated Product-Service Systems concept orientated toward selling product functionality instead of selling products, are gaining increased attention. With more complex production environments maintenance must be viewed as a value-adding service, but this requires new business models and new ways to design service agreements. Maintenance has traditionally been regulated based on fixed price on predefined work, which represents a product-centred view on service. A value-oriented contract form is based on the performance or utility maintenance can provide. Many companies have not yet understood the business opportunities that could be achieved by providing maintenance services, and efficient strategies for enabling these opportunities are lacking. Moreover, many believe that you have to choose either or, thus either one provides product-oriented maintenance or value-oriented maintenance.

This paper proposes an integrated, dynamic and modular approach to maintenance business model development. Modular-based maintenance offerings classify maintenance services with increasing integration of the offering, and increasing focus on utility for the customer and the customer’s customer. Moreover, modular-based maintenance offerings allow for flexibility; one does not have to choose between the product-centred or utility-centred business models. Instead, the offering is packaged based on the available internal resources and key capabilities of the service provider which are matched against specific customer needs. The dynamics of the maintenance offerings are the time and scope dimensions describing the boundaries in which maintenance execution could take place at the customer.

ABSTRACT. Different methodologies are nowadays employed to identify failure events in in-dustrial process, allowing the decision makers to choose appropriate technical and organizational safety measures. The treatment of data in order to prevent dan-gerous events may affect significantly the diverse analyses and is reflected in the final results. Quantification risk analysis is therefore one of the most critical areas in asset management (AM) as stated in the ISO 55000. In the same way, intelli-gent risk management should be one critical challenge of the Industry 4.0, since nowadays and by using new technologies, it is possible to gather large amounts of data extrapolated from the physical assets. With all the above, this paper is intended to understand uncertainty, trying to re-duce the risk of dangerous events by the treatment of big data. Particularly, a time window is obtained showing minimum and maximum thresholds for the best time to apply a preventive maintenance task, together with other interesting statis-tics.

ABSTRACT. Our society-increased dependence on utilities performance fosters the need for this infrastructures resilience. Especially in networks utilities, costs of incidents can enormously propagate throughout the network, affecting numerous customers and impacting business results, image and reputation.

At present, measures of resiliency or survivability are not properly established within utilities. In this work, we first present a review about existing approaches to tackle network utilities survivability; then, focusing on graph theory principles, a new model is proposed to evaluate this feature. The model allows the comparison of different networks’ survivability, and may serve as a valuable tool to support networks services management along their life cycle.

ABSTRACT. Online monitoring (OLM) analyses data collected during plant operation to support non-intrusive calibration and health assessment for nuclear power applications. OLM provides technical and economic advantages over current time-based inspection practices in the current fleet of light water reactors and can improve the viability of future small modular and advanced reactor designs. Although the OLM concept has been generally accepted by the Nuclear Regulatory Commission, OLM implementation in US power plants is currently limited due to technical constraints associated with the predictive uncertainty quantification necessary to meet regulatory requirements. A Bayesian inference technique has been developed to accurately quantify uncertainty in OLM predictions with applications here to sensors calibration assessment. Various optimization techniques are explored for hyperparameter estimation for the Bayesian inference model.

ABSTRACT. Maintenance at nuclear power plants, and in many other industries, is usually performed by time-based schedules or after specifications from equipment manufacturers and by monitoring the condition of equipment. Research at IFE addresses condition-based maintenance with prognostic estimation of the degradation of process components and calculation of remaining useful life (RUL) based on measurements and selection of health indicators for the components. Prognostic models have been developed through case studies for air and sea filters at nuclear power plants and choke valves from off-shore industry. Physical models for plant thermal performance have been developed for monitoring and optimizing operation using data reconciliation.

With increasing amounts of data and new methods to analyse large and complex data sets using machine learning and big data analytics, the new technologies are now being used to solve numerical problems that previously would require high performance computing and at considerable cost. Currently many of the nuclear power plant worldwide are reaching the lifetime they were designed and licensed for, and many of them have started a process to extend the operational lifetime of these plants. Historic measurement data gives knowledge about degradation of process components, and increased understanding of degradation mechanisms can be utilized for maintenance plan optimization. Equipment health indicators from various sources will be combined to analyse the condition of safety related and non-replaceable components to estimate the future condition of the plant. Both physical and statistical methods can be combined in hybrid models, where possible, to carry out the calculations.

ABSTRACT. The Light Water Reactor Sustainability (LWRS) Program is a research and development (R&D) program sponsored by the U. S. Department of Energy (DOE) and performed
in cooperation with the related R&D programs of the nuclear industry and the
U.S. Nuclear Regulatory Commission (NRC). The LWRS Program provides technical foundations for licensing and managing the long-term safe and economical operation of current nuclear power plants.

The LWRS Program has two facets: (1) understand and manage the aging of nuclear power plant systems, structures, and components (SSCs) and how to best manage them so that the plants can continue to operate safely, efficiently and economically; and (2) deploy innovative approaches to improve economics and economic competitiveness of plants in the near term and in future energy markets. The program's R&D role addresses issues that require long-term research and unique expertise and facilities to address a broad range of operating reactors.

The LWRS Program demonstrates a number of the issues facing asset owners and operators of power generation facilities both as they age and as they encounter new challenges in commercial markets over their lifetimes, and how science-based solutions can be used as a means to ensure their continued safe operation.

ABSTRACT. Digitalization is one of the key technology to improve safety and performance of Nuclear Power Plants (NPP). EDF has initiated research and development in this field for more than 10 years and some tools are now commonly used by EDF operators. They cover outage management (use of virtual reality for outage preparation, 3D visualization of plant maintenance and upgrade…), human performance in operation (plant field workers mobile technologies, augmented reality to improve situation awareness, advanced training…), advanced plant control automation and digital architecture (fab lab for design and fast checking of future concept of operation…), as well as on-line equipment and process monitoring. Now the new challenges are to develop “twin reactors” which are digital models fed by on-line data from operation and to benefit from data analytics science which made great progress during the last years. EDF has engaged new programs in these fields that will support long term operation of most critical nuclear equipment. A first development, dedicated to a 1:3 scale model of a NPP containment is now operational. It gives an idea of the technical challenges to handle before deploying such technology for EDF fleet.

ABSTRACT. Layer of protection analysis is a popular risk assessment tool to study severe hazard scenarios quantitatively in petroleum industry sector. Human interventions and performances are normally included in the analysis. Human beings may function as independent protection layer (IPL) or serve part of the protection layer functions during the prevention of specific hazards. A natural gas transportation pipeline section with redundant shutdown systems is studied. The forms and impacts of human activities influences with regard to related protection layers are identified and analyzed. The paper studies how independent condition based performance monitoring systems could play as a performance shaping factor that may affect human interventions in terms of detection of deviation, decision-making and response. Implementations of LOPA with and without condition monitoring systems are carried out to reflect the influence.

ABSTRACT. Asset management is a dynamic process. In such a dynamic operating environ-ment, it is quite challenging to keep track and visualise asset integrity-associated risks. One of the main concerns for all assets is keeping unexpected downtime to a minimum thereby maintaining highest availability. An application, Dynamic Risk Simulator (DRS), was developed to simulate the lifetime of single-unit repairable systems. Purpose of this application is to be able to capture reliability parameters based on equipment age and maintenance/repair history. The application uses proven statistical methods in combination with practical input from maintenance engineers from a Maintenance Modification & Operations (MMO) organization (Apply Sørco AS). The application calculates availability dynamically while vary-ing input parameters within select preventive maintenance strategies. This is per-formed by simulating the life of the asset in finite time horizon taking into ac-count its age, preventive maintenance strategy and its useful life. The application also provides estimation of maintenance costs, optimum maintenance interval and equipment availability. The purpose of such an application is to support decisions in appraising opportunities related to reviewing and updat-ing existing maintenance strategies. The application was partially validated with data from an offshore asset provided by Apply Sørco. The limitation of DRS is that it cannot replace human judgement with regards to taking the final call on whether or not to postpone maintenance. DRS provides quantitative and qualita-tive results and is reliant on the experience and insight of industry experts to take the most appropriate course of action based on the provided input.

ABSTRACT. Large utilities need to optimize the investment made to maintain their assets. For a utility like Hydro-Québec (37 GW) an important part of those investments are made to maintain their hydroelectric facilities. To minimize the maintenance cost, technico-economic model enabling the propagation of uncertainty associated with the degradation processes of a given component seems essential. Therefore, for Francis hydroelectric turbine runners, we developed two technico-economic models: one for crack propagation and one for cavitation. Since these are the main degradation mechanisms leading to failure of Francis runners, they enable us to study the effect of maintenance strategies on the maintenance cost of these components. The model has been created using VME, an asset management software developed by EDF R&D (Électricité de France). VME uses Monte-Carlo simulations to generate stochastic failure dates and obtains probabilistic indicators of the net present value of a given management strategy. We will use a study case based on a Hydro-Québec (Québec, Canada) facility to illustrate the importance of the proper assessment of current and expected long-term reliability on maintenance cost.

The paper will be structured as follows. First, an overview of the modelling strategy will be presented. Then, we will have a closer look on how VME, the tool used for Monte-Carlo simulations, derive its results. Finally, we will present a study case and discuss the results obtained in terms of the sensitivity to the reliability assessment uncertainties.

ABSTRACT. Cosmo Tech is a global technology company that helps the C-suite make optimal decisions. Through our unique methodology, we model and then simulate complex scenarios to accurately predict the outcome of events even if those events have never happened before. And we deliver interconnected insights that tell you how each part of your organization would be affected by your potential choices so you can make the best decisions for your company. What we do goes way beyond big data and data science to decision management and augmented intelligence. We transform companies by giving them reliable insight into the future. Our presentation will cover the Technical solution developed by COSMO TECH, as well as a couple of Business cases and results.

ABSTRACT. Industry 4.0 is an industrial era where several disruptive technologies e.g. Inter-net of Things, cloud technology, 3D printed, advanced robotics and materials have merged or integrated to enables a new level of organising and controlling the entire valve chain with the product lifecycle by creating dynamic and real-time understanding of cross-company behaviours. Such collaboration between physical and cyber spaces is rapidly growing and the benefits becoming more feasible. However, the interaction of human (in case automation is not applica-ble) at physical space with digital contents requires special facilitation where ar-gumentation is the key. Predictive maintenance which is based on real-time measurements e.g. health/process parameters requires high level of expertise to understand and diagnose the present status of machine health. Therefore, the purpose of this paper is to explore, through a pilot project, how could augmented reality technology be utilised to assist novice learners to gain deep understanding of predictive maintenance process and practical skills as well. The case study is developed to assist the student of industrial asset management programme to learn the machine fault simulator and the analysed content of vibration meas-urements. Such contribution aims to enhance the training technology and accel-erate the learning process of novice technician/engineers to be expert.