ABSTRACT. Due to missing measurement data, degradation prognoses are usually not possible in early design stages of products. Transfer Learning (TL) uses information from comparative problems for modeling and is therefore a promising approach for degradation prognosis under a lack of data. A procedure was developed with which the problem-specific suitability of TL methods can be assessed stepwise, considering the requirements and the available data for the implementation of a degradation prognosis. The procedure enables a preselection of suitable TL methods and should increase the performance of the created model. To evaluate this procedure, it is applied on the example of the wear of spline joints with involute flanks according to DIN 5480 or ISO 4156 at different loads and fits. Measurement data from wear tests collected in past projects will be used for this purpose. Based on the considered procedure, a TL method is selected and implemented for the spline joints. A part of the measurement data is used to train the TL model. The remaining degradation data is used to validate the prognosis accuracy of the generated degradation model and to evaluate the performance of the developed selection procedure and to identify potential improvements.

ABSTRACT. Efficient and robust fault detection in PHM typically depends on high-quality run-to-failure data, yet such labeled historical data is often scarce. While unsupervised algorithms trained on data assumed to represent normal operation (such as early-stage operation data) can yield good performance, they may be limited by contamination or incomplete coverage of normal states, related to unseen operating scenarios and conditions. This study investigates the effects of incomplete coverage of normal scenarios on fault detection performance. To enhance robustness against such incompleteness without making assumptions on the dataset’s composition, employing weakly supervised anomaly detection by incorporating few labeled fault samples is proposed. Evaluations on degradation testing data show that providing an autoencoder with a few fault labels significantly improves detection performance compared to a fully unsupervised autoencoder. This improvement is shown to be robust under various conditions, including changes in the number and quality of labeled fault samples and variations in the training data's composition. The proposed approach realistically simulates incomplete coverage through selective dataset composition and builds on weak supervision to enhance fault detection, making it particularly suitable when only field data – with no information on its composition - and few labeled fault samples are available.

ABSTRACT. Hybrid energy systems that combine solar photovoltaic (PV) and wind turbine (WT) technologies are increasingly adopted to enhance renewable energy production. Ensuring the reliability and efficiency of these systems necessitates effective maintenance strategies to prevent unexpected failures and optimize operational performance. This paper proposes a predictive maintenance framework for hybrid solar and wind energy systems, leveraging machine learning algorithms to forecast component failures and schedule timely preventive maintenance actions. By analysing historical performance data and environmental conditions, the model predicts the Remaining Useful Life (RUL) of critical components, enabling proactive maintenance planning. The integration of machine learning techniques facilitates the dynamic assessment of component health, leading to reduced downtime and maintenance costs. A case study demonstrates the application of the proposed framework, highlighting its effectiveness in enhancing system reliability and extending the lifespan of hybrid renewable energy installations.

ABSTRACT. Proton Exchange Membrane Fuel Cells (PEMFCs) play a critical role in zero-emission electro-hydrogen generators (GEH2). Accurate remaining useful life (RUL) prediction for PEMFCs is essential to optimize operational efficiency and enable cost-effective predictive maintenance. A key challenge in RUL estimation lies in selecting an effective Health Indicator (HI) that reliably reflects the degradation state of the PEMFC stack. While voltage and power-based HIs are commonly used under static conditions due to their monotonic decline and ease of measurement, they become unreliable under dynamic operating conditions due to their sensitivity to mission profiles. To address this issue, we propose a machine learning model to extract a robust HI from voltage measurements, capturing the true degradation state of the PEMFC. Building on this HI, we propose a hybrid deep learning architecture combining Transformer networks and Gated Recurrent Units (GRUs) to model temporal dependencies in PEMFC degradation and predict the RUL of PEMFCs operating under dynamic conditions. The proposed approach is validated using a real industrial dataset comprising four PEMFCs. Comparative analyses against state-of-the-art machine learning methods demonstrate that our approach consistently outperforms existing models, exhibiting superior predictive accuracy and robustness. These results highlight the framework’s robustness in real-world scenarios, offering a scalable solution for predictive maintenance of PEMFC systems under dynamic mission profiles.

ABSTRACT. Accelerated life tests are experimental methods used to estimate the lifespan or reliability of a product, component, or material by subjecting it to conditions more severe than normal operational environments. The goal is to induce failures more quickly, so that data can be collected in a shorter time, allowing engineers and designers to predict the product's performance under normal conditions over a longer period. The main objective of the present study is to define the lifetime of supercapacitors through accelerated life testing. An introduction is made regarding the mathematical characteristics of accelerated life tests applied to innovative energy storage technologies. To validate and implement the presented concepts, the referred tests were conducted on supercapacitor prototypes, subjected to various voltages, exceeding normal operating levels, to accelerate their degradation and induce failure, allowing to predict the lifespan of such devices under normal operating conditions. The experimental data collected in the study was obtained by carrying out floating tests, that were fitted to an appropriate degradation model and analyzed. Results showed that this model can be applied to supercapacitors' accelerated life tests, allowing the comparison of different materials and obtaining the best solutions.

ABSTRACT. Accurate prediction of when a track asset requires renewal is crucial in any efficient management strategy, to ensure that adequate funding for replacements is available when needed. The end of an asset’s useful lifetime can be defined by multiple criteria, including the amount of maintenance works completed, the frequency of interventions, the total cost of maintenance, and the overall condition of the asset. This study develops a Petri net model to predict the lifespan of a component on the railway line, given the renewal criteria. The approach consists of modelling the inspection, degradation, maintenance, and renewal processes via sub-models, with strategic placing of tokens to indicate the chosen renewal strategy. The thresholds for maintenance, including the required action and its expected timeframe, are taken directly from railway operational standards, and the expected degradation events are based on a reliability study of historical track failure data. The model has been computed via Monte Carlo simulation, using data from the HS1 railway line, situated in the UK. The results from this study aim to support the railway industry when selecting the most cost-effective renewal strategy that keeps the track asset in a safe, practical condition for the longest time.

ABSTRACT. Multi-stack Proton Exchange Membrane Fuel Cells (PEMFCs) have gained significant attention due to their high efficiency, scalability, and flexible operation, making them a promising solution for carbon-free, high-power applications. However, durability challenges remain a major barrier to commercialization, as fuel cell degradation directly impacts system reliability and operational costs. Prognostics and Health Management (PHM) approaches have shown great potential in extending fuel cell lifespan by enabling predictive maintenance and optimized load allocation strategies. This paper proposes a load-dependent degradation model based on a non-homogeneous stochastic gamma process to predict PEMFC degradation under varying load conditions. The model effectively captures uncertainties, variability, and non-linear degradation dynamics, providing a more accurate estimation of Remaining Useful Life (RUL). Leveraging this model, a health-aware energy management strategy is developed for multi-stack configurations, dynamically adjusting power distribution to mitigate degradation effects. Additionally, an optimal maintenance strategy is introduced, incorporating replacement decisions to minimize operational costs while maintaining system reliability. The proposed methodology is evaluated against traditional energy management approaches, demonstrating its potential to significantly enhance PEMFC durability and cost-effectiveness in real-world applications.

ABSTRACT. Accurate crack depth detection is critical for ensuring structural integrity and reliability in engineering systems. However, inspection errors such as false positives (FP) and false negatives (FN) can significantly affect maintenance decision-making and risk evaluation. This study presents a probabilistic framework for modeling FP and FN occurrences in crack-depth detections considering their time-dependent nature. The proposed model enhances both theoretical understanding and practical applications by integrating uncertainty quantification into structural health monitoring and non-destructive testing. The study explores the applicability of exponential models in describing crack growth. Exponential growth models are widely used in reliability engineering due to their ability to capture the stochastic nature of material degradation and fatigue crack propagation. By incorporating time-dependent FP and FN probabilities, the proposed approach improves on conventional static models, offering a more accurate representation of inspection reliability over an asset’s operational lifespan. The introduction of time-dependent FP and FN probabilities provides several advantages. First, it allows for dynamic updating of detection reliability, leading to optimized maintenance schedules and reduced unexpected failures. Second, it facilitates risk-informed decision-making by quantifying the probability of FP and FN at different operational stages. Third, it supports decision processes, enabling various maintenance strategies based on evolving crack conditions. Practical applications of this model extend to reliability engineering, aerospace maintenance, and industrial asset management. By integrating the probabilities of FP and FN into reliability assessments, engineers can achieve more robust risk mitigation strategies, ultimately improving safety and cost efficiency in engineering systems. These results underscore the importance of incorporating time-dependent error modeling in crack assessment, paving the way for more resilient and adaptive maintenance methodologies.

ABSTRACT. An effective predictive maintenance plan ensures operational reliability while achieving key optimization goals. In the context of Gas-Insulated Substations (GIS), our core concerns lie in minimizing maintenance costs and mitigating the environmental impact of sulfur hexafluoride (SF6) gas leakage, a potent greenhouse gas. Building on our previous work optimizing maintenance schedules for individual GIS components, this study extends the approach to a second-stage task-grouping optimization consolidating maintenance tasks across multiple GIS components to address operational inefficiencies such as increased costs, underutilized resources, and prolonged downtime. Specifically, our approach combines maintenance tasks that are temporally close and operationally compatible, thereby reducing travel costs and improving resource utilization. The proposed method formulates the grouping problem as a Mixed Integer Linear Programming model, incorporating key constraints such as task time windows, resource availability, and operational priorities. Furthermore, the trade-off between maintenance costs and SF6 leakage quantity is analyzed to account for its environmental impact.

ABSTRACT. Motivated by the Right-to-Repair legislation, we propose a data-driven warranty pricing model that uses non-life insurance techniques. Our proposed model aims to address the complexity involved in determining warranty fees, which are influenced by two main factors: unpredictable return patterns and stochastic remanufacturing costs based on product quality. To address these challenges, we use historical return data and employ a survival regression model based on product characteristics to predict the time until a product is returned. Using the same product characteristics, a gradient-boosting machine predicts the remanufacturing costs of a return. The combination of these two techniques enables the customization of the warranty fee based on the product characteristics. We validate our approach using a dataset of about 35,000 returns over a five-year period from an automotive parts supplier. Our model provides a framework for industrial companies to manage return management strategies and warranty pricing.

ABSTRACT. Reconfigurable Manufacturing Systems (RMSs) are designed to adapt to fluctuating market demands and evolving product configurations. Unlike traditional manufacturing systems, RMSs are characterized by their ability to reconfigure in response to changes in production capacity and product design. However, maintaining these systems presents significant challenges due to their modular, scalable and dynamic nature. Conventional maintenance models do not fully exploit the unique modularity, scalability, customization and convertibility of RMSs, leading to suboptimal maintenance strategies and increased downtime. Although maintenance is critical for improving the performance and adaptability of RMSs, most existing research has focused primarily on their design and operation, leaving a gap in the literature. To address these limitations, this research proposes a novel maintenance framework considering key RMS characteristics, providing a structured methodology for maintenance modeling and decision-making. This framework allows the designing of maintenance strategies in RMSs to enhance system reliability and responsiveness. Various case studies were then conducted to investigate the feasibility and the robustness of the proposed framework, demonstrating its practical use in RMS maintenance planning and serving as an adequate tool for engineers and practitioners aiming to optimize maintenance in RMS environments.

ABSTRACT. The study presented in this paper assess failure incidents and predict maintenance requirements for Electric Multiple Units (EMU) trains using time series forecasting techniques. A Seasonal Autoregressive Integrated Moving Average with Exogenous variables (SARIMAX) model approach was utilised, and an attempt is made to forecast the patterns of failure incidents using weekly and monthly CCTV failure data. The Augmented Dickey-Fuller (ADF) test is used for stationarity assessments of the failure data, while autocorrelation and partial autocorrelation function is used to determine the seasonality. Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) are applied to ensure model selection balances statistical accuracy and complexity, preventing overfitting SARIMAX risk-based model approach is used to forecast the weekly and monthly CCTV failure incidents and then categorise the risk of failure of CCTV systems into low, medium and highrisk levels, represented with a traffic light system. Graphical display of the model simulation results from three geographical locations in Scotland is presented in this paper. The outcome of this paper is to support informed decisions on maintenance scheduling, and the allocation of maintenance resources based on the risk of failure.

ABSTRACT. Comparisons between Condition Based Maintenance (CBM) and Time Based Maintenance (TBM) are often made under a fixed cost assumption. However, wind speed is seasonal, hence opportunity cost of maintaining wind turbines follow a cyclic pattern too. In this talk the fixed cost assumption is therefore abandoned. To compare CBM and TBM, for both methods a discrete Markov Decision Process (MDP) is formulated, which is solved by means of linear programming. The one-step transition probabilities of the deterioration process are derived from a stationary discrete gamma process. When cost is time-varying, the optimal threshold policy for both TBM and CBM becomes dependent on the week of the year. The majority of preventive maintenance actions is conducted in campaigns at the cheapest moment of the year. It is found that CBM always outperforms TBM, also under time-varying cost. For a low Mean Time To Failure (MTTF), the relative performance of TBM improves when seasonality increases.

ABSTRACT. This study presented in this paper investigates failures contributing to increased downtime in an LPG plant gangway system. It aims to identify critical failure modes, assess the effectiveness of current maintenance strategies, and propose improvement initiatives to minimise downtime. The analysis utilises historical failure and maintenance data collected over a 4-year period. An exploratory analysis of the data is conducted using advanced statistical methods, and a failure mode and effect analysis approach is used to identify the critical failure modes and corresponding areas where the failure occur are classified as mechanical and instrumentation. The probability of failures and their impact on system reliability is calculated. Reliability analysis is conducted using the Fault Tree Analysis (FTA) approach. Inferential statistics are employed to evaluate the severity of downtime events systematically. The result provides an insight into more efficient operational and maintenance planning for the LNG plants critical systems.

ABSTRACT. The paper main objective is to determine if the maintenance effect, in multicomponent series systems, is dependent on the reliability characteristics of the replaced component(s). To do so, a series system composed of six independent and heterogeneous components is studied. The reliability of this system and that of its components are assumed to follow the two-parameter Weibull distribution with an increasing failure rate. The system maintenance efficiency is modeled using the three-parameters hybrid imperfect maintenance model. The obtained results indicate that the maintenance effect is correlated with some reliability characteristics of the replaced components.

ABSTRACT. Shannon introduced entropy in his seminal 1948 article entitled ``A mathematical theory of communications''. This notion has been used in a few papers at the turn of the century, for instance for the performance assessment of intelligent machines. By contrast, there has been in the last five years a fast growing literature on the application of Shannon entropy and its variants to system maintenance in various domains: radars, metro signaling, motor fault detection, etc. Yet the described systems are mostly either small or have a simple series or parallel architecture.

The purpose of this paper is to consider the different versions of entropy used in maintenance studies and compute their values for large and possibly meshed networks. Asymptotic expansions as a function of the system size are given and explained for various architectures of practical interest. The influence of the failure distribution density --- not necessarily exponential --- is also addressed, along with the changes brought by the introduction of common-cause failures.

ABSTRACT. Maintaining consistent performance and reliability in an industrial facility built in the 1940s presents unique challenges. Aging infrastructure, evolving maintenance and reliability standards, evolving maintenance and reliability standards, and technological advancements necessitate a strategic asset management approach. This presentation explores best practices for sustaining asset reliability in an aging facility through a structured methodology, that complies to the Total Plant Reliability Management (TPRM) requirements. Additionally, it highlights how technological advancement, and proactive reliability culture contribute to long-term asset sustainability. By attending this presentation, participants will learn how to develop and implement effective strategies for optimizing asset performance, reducing downtime, and extending the lifespan of their facilities, ultimately achieving operational excellence and maximizing return on investment

ABSTRACT. Rotating equipment, such as pumps, gearboxes, and turbines, rely heavily on proper lubrication to ensure efficient operation and longevity. However, lubrication tanks can be susceptible to contamination from airborne particles, moisture, and other environmental factors, which can compromise the integrity of the lubricant and ultimately lead to equipment failure. Breathers play a vital role in preventing these contaminants from entering the tank, thereby safeguarding the quality of the lubricant and protecting the equipment. By allowing air to enter and exit the tank while filtering out impurities, breathers help maintain a stable atmosphere within the tank, reducing the risk of oxidation, corrosion, and microbial growth. This paper highlights the significance of breathers in rotating equipment lubrication tanks, discussing their design, functionality, and benefits, as well as best practices for selection, installation, and maintenance. By emphasizing the importance of breathers, this research aims to raise awareness among industry professionals about the critical need for effective contaminant control measures to optimize equipment performance, reduce downtime, and extend lifespan.

ABSTRACT. Effective maintenance strategies are essential for ensuring sustainability and cost efficiency in complex systems with heterogeneous components. A well-balanced maintenance policy must consider economic, social, and environmental factors by minimizing urgent interventions and reducing unnecessary replacements. By prioritizing planned maintenance over reactive tasks, organizations can lower maintenance costs, improve system reliability, and reduce after-hours workload, enhancing both operational efficiency and employee well-being. To achieve this, we propose a flexible maintenance policy that integrates condition-based maintenance (CBM) and failure-based maintenance (FBM), leveraging opportunistic maintenance to optimize intervention timing. Unlike traditional fixed-interval approaches, our method strategically incorporates semi-urgent and urgent visits as opportunities for preventive replacements, reducing reliance on costly urgent interventions.

To determine optimal maintenance actions, we formulate a Markov decision process (MDP) and address computational complexity through heuristic approaches. We propose two heuristics: Heuristic 0, which relies solely on corrective maintenance and incurs higher costs, and Heuristic 1, which incorporates semi-urgent and opportunistic maintenance to improve cost efficiency, sustainability, and computational feasibility. Numerical experiments demonstrate that the proposed policy significantly reduces maintenance costs, urgent interventions, and material waste. This study provides a scalable and cost-effective solution for industries aiming to enhance maintenance planning, system reliability, and sustainability by aligning economic, social, and environmental objectives.

ABSTRACT. As industries face increasing pressure from climate change and demographic shifts, traditional performance metrics like Overall Equipment Effectiveness (OEE) prove insufficient for addressing long-term sustainability. While OEE effectively measures productivity, it does not integrate environmental, social, and economic considerations that are crucial for sustainable operations. To bridge this gap, we propose the Overall Sustainable Equipment Effectiveness (OSEE) framework, which extends conventional performance evaluation by incorporating sustainability dimensions. The framework is developed through a comprehensive literature analysis and refined through expert validation to ensure applicability in real-world industrial settings. To navigate the complex interdependencies of sustainability factors, we employ causal AI methods, specifically Dynamic Bayesian Networks (DBN) and Knowledge Graphs (KG). These tools enable a structured representation of cause-effect relationships within sustainability contexts while facilitating quantitative optimization of their impacts on operational efficiency. By integrating AI-driven insights, the OSEE framework provides decision-makers with a systematic approach to balancing efficiency and sustainability. This research contributes to the broader twin transition, linking digital transformation with sustainability efforts to create resilient and future-oriented industrial systems.

ABSTRACT. Flexible manufacturing systems (FMS) face escalating challenges due to the vast number of components and potential failure causes, intensified by diverse product portfolios and fluctuating production volumes. This paper introduces an agent-based adaptive recommendation system that signifies a paradigm shift in data-driven maintenance management. By integrating the expertise of maintenance engineers and personnel, the system employs a Natural Language Processing (NLP)-based model to classify symptoms, causes, and solutions with high accuracy (achieving 100 % for symptoms, 90 % for causes, and 85 % for solutions). Leveraging Bayesian networks to model cause-and-effect relationships, the system enables probabilistic inferences and provides resource-efficient recommendations that minimize energy use, resource consumption, and waste. Continuous feedback mechanisms enhance the system's performance over time, improving diagnostic and repair processes while promoting sustainable maintenance practices.

ABSTRACT. Optimal cutting tool replacement policy is an essential subject in machining that balances the risk of producing out-of-tolerances pieces, and avoids excessive consumption of new tools and production interruption. The tool replacement policy is usually determined by striking an economic balance that does not consider the environmental impact of the policy and this situation contradicts the principles of the circular economy, which are now essential. In industrial practice, this often results in systematic preventive replacement of tools after a safely fixed number of produced pieces, wasting a portion of the tool's useful life (at an environmental cost) and increasing the machine's downtime. In this paper, based on advances in cutting tool monitoring, we assume that it is possible to monitor the tool wear. Under these hypotheses, we use a Monte-Carlo simulation method to show, through an example in Ti6Al4V milling, that a condition-based replacement allows improved economic and ecological performance, considering the single indicator of Global Warming Potential, expressed in kgeq CO2, of the tool replacement policy, even under uncertainty on the condition estimate.

ABSTRACT. Planning in industrial settings occurs across various levels to ensure efficiency and effectiveness in operations. These levels are typically categorized into three tiers: strategic, tactical, and operational. This classification holds true for both production and maintenance planning processes.

At the strategic level, plans are crafted with a long-term perspective, spanning months to years. This involves setting overarching goals, identifying major milestones, and aligning resources accordingly. Tactical planning, on the other hand, deals with a medium-term horizon, usually encompassing weeks. During this phase, detailed plans are formulated to achieve the broader strategic objectives, taking into account factors such as market trends, resource availability, and capacity constraints. Finally, operational planning addresses short-term needs, focusing on daily or weekly schedules to ensure smooth execution of tasks on the shop floor.

Traditionally, these three levels were deemed sufficient for effective planning. However, in the realm of production planning, it became evident that the actual execution of planned activities often deviates from the meticulously crafted plans. Various unforeseen factors, such as machine breakdowns or workforce shortages, can disrupt the execution process, necessitating real-time adjustments. This realization led to the recognition of an additional planning level: the execution level. Unlike the strategic, tactical, and operational levels, which primarily involve planning activities, the execution level involves monitoring and adapting plans in real-time based on the unfolding events on the shop floor. It acts as a feedback loop, enabling organizations to respond promptly to deviations from the original plan and optimize resource utilization.

Interestingly, while production planning has embraced this concept of an execution level, the same cannot be said for maintenance planning. Despite the prevalence of corrective maintenance activities in machine-intensive industries and the advent of modern technologies such as Industry 4.0, maintenance planning has yet to incorporate a dedicated execution level. This is particularly surprising given the increasing emphasis on real-time monitoring and condition-based maintenance, facilitated by advancements in AI and machine learning. These technologies enable organizations to predict machine failures, schedule maintenance activities proactively, and minimize downtime. Thus, there is a growing need to integrate an execution level into maintenance planning, enabling organizations to respond swiftly to maintenance-related disruptions and optimize asset performance.

Moreover, the lack of alignment between production and maintenance plans represents a significant challenge for organizations. Production and maintenance activities are inherently interconnected, as both rely on the availability of machinery and skilled labor. However, if these activities are not synchronized, it can lead to inefficiencies and resource wastage. For instance, scheduling maintenance during peak production periods can disrupt operations, while neglecting maintenance can result in unplanned downtime and costly repairs. Therefore, there is a compelling case for aligning production and maintenance planning efforts to optimize resource utilization and minimize disruptions.

In light of these challenges and opportunities, this thesis proposes a comprehensive framework for real-time maintenance planning. By leveraging insights from production scheduling, AI-driven predictive maintenance, and resource allocation techniques, the framework aims to enable organizations to make informed decisions on when and how to execute maintenance activities. It takes into account various factors, including the system's current state, incoming maintenance tasks, and available resources, to ensure optimal asset performance and operational efficiency.

ABSTRACT. This study focuses on predicting key quality indicators of industrial processes. In real-world applications, these indicators are crucial not only for assessing product quality but also for driving process control. Accurate and timely predictions are essential for ensuring the successful implementation of these processes. However, most current approaches rely on analytical models with simplifying assumptions, often rendering them inaccurate and rigid. These models struggle to capture and predict drifts in quality that arise from deviations from ideal conditions. This work addresses these limitations by adopting a data-driven approach that leverages the abundance of historical production data. In the context of steel manufacturing, notably the Hot Strip Mill (HSM) process, the coiling station temperature (CT) is used as an indirect gauge of material properties and, by extension, product quality. Current methods predict CT using production parameters (material composition, exit mill temperature, speed, heat of transformation and water flow distribution in the Run-Out-Table) as inputs to analytical models based on first principles, such as heat transfer and phase transformation. However, surface quality also influences CT due to its measurement via infrared pyrometers (IR). Surface defects create fluctuations in CT measurements, making it a potential indirect indicator of surface quality. Unfortunately, the simplifying assumptions of analytical models prevent them from accounting for these fluctuations, resulting in inaccurate CT predictions and missed opportunities to assess surface quality—a critical factor for steel users. This study overcomes these challenges by adopting a data-driven approach that relaxes the assumptions of traditional analytical models. Historical production data inherently capture the relationships between process parameters and quality indicators. Data-driven models, particularly neural network regressors, are flexible enough to uncover these connections without predefined assumptions, enabling them to account for most quality deviations and provide more accurate, comprehensive predictions. To achieve this, production data for a selected steel grade that presents CT fluctuations were collected and curated. Neural network regressors were trained using the curated production data, with custom loss functions and performance metrics designed to improve prediction accuracy. These tailored tools address the challenge posed by data distributions that are centered on normal values, where deviations rarely occur. The custom functions and metrics ensure precise capturing and identification by targeting these rare but critical deviations. They help minimize the risk of misidentifying significant temperature anomalies, further enhancing the accuracy of the predictions. Physics and domain knowledge were also integrated into the data-driven models to further improve performance and reliability. The resulting CT predictions can enable high-performing proactive control and pave the way for improved process efficiency and product quality.

ABSTRACT. Ensuring trustworthiness in electronic systems is crucial to maintain safety and data integrity. Safety properties of robotic components are rigorously validated during development and, similarly, security requires ongoing monitoring during system operation as well. However, this monitoring must also safeguard its own components from tampering.We propose a novel runtime monitoring approach using application-specific monitors within an FPGA-based Trusted Execution Environment (TEE). To protect these monitors from supply chain attacks during design, fabrication, testing, or packaging, the TEE is programmed as the final step before deployment. The monitors are directly generated from formal constraint specifications established during the design and test phases. Our approach is demonstrated on a RISC-V-based System-on-Chip (SoC) for robotic applications, featuring a force sensor and a CAN-bus interface. We monitor the timing behaviour of hardware and software to detect malicious modifications affecting data transmission to a control unit. In an FPGA prototype, the monitors successfully identified hardware and software tampering. In real ASIC implementations, programming the TEE post-packaging ensures resilience against supply chain attacks.

ABSTRACT. Maintenance of industrial rotating and stationary equipment requires innovative approaches to achieve high reliability and operational efficiency. This presentation focuses on maintenance techniques such as reverse engineering, high-performance thermal coatings, and modern planning tools that revolutionize repair and upkeep processes. Case studies will illustrate successful implementations of these techniques, highlighting their contribution to reducing downtime, improving asset reliability, and aligning maintenance with long-term industrial goals. This comprehensive approach empowers industries to transition from reactive to reliability-focused maintenance strategies, driving sustainability and competitiveness.

ABSTRACT. Water distribution networks (WDNs) face increasing challenges due to urbanization, climate change, and aging infrastructure. This study presents a probabilistic framework to evaluate and enhance WDN resilience under multiple failure scenarios. Traditional pipe criticality assessments overlook dynamic hydraulic conditions; therefore, we integrate real-time pressure indicators to improve vulnerability analysis. Hydraulic simulations are conducted in EPANET, and pipe failure scenarios are implemented in a bespoke Python-based research code to assess network performance over time. A probabilistic failure and recovery model is developed to capture uncertainties in response times and operational constraints. Results of two case studies demonstrate that incorporating pressure-based rankings improves maintenance prioritization, shortens recovery times, and enhances resilience planning. The proposed framework provides water utilities with a robust decision-making tool to optimize resource allocation and maintain service continuity during disruptive events.

ABSTRACT. Disruptions on a rail network can halt operations at affected sections, leading to the rapid propagation of delays throughout the highly congested network, as observed in the UK rail network. Therefore, timely responses to disruptions are essential to minimising the delay propagation. This paper proposes a hybrid simulation-optimisation model that employs genetic algorithms to automate real-time decision-making for disruption management. This research aims to determine optimal service alterations for targeted train services, facilitating faster recovery while minimising total delay minutes, additional costs and the number of train cancellations across the network. The results generated by the model will be compared with the prewritten contingency plans used by network operators to evaluate model effectiveness. The expected result should balance the three key aspects under consideration while adhering to specific operational constraints to ensure practicality. The desired findings include identifying the characteristics of train services that should be altered during disruptions and quantifying the improvements achieved by the model. In summary, this research contributes to strengthening the resilience of the rail network by enhancing its ability to recover from disruptions efficiently.

ABSTRACT. Disruptions on a rail network can halt operations at affected sections, leading to the rapid propagation of delays throughout the highly congested network, as observed in the UK rail network. Therefore, timely responses to disruptions are essential to minimising the delay propagation. This paper proposes a hybrid simulation-optimisation model that employs genetic algorithms to automate near real-time decision-making for disruption management. This research aims to determine optimal service alterations for targeted train services, facilitating faster recovery while minimising total delay minutes, additional costs incurred due to disruption management and the number of train cancellations across the network. The performance of the model is evaluated by comparing its outcomes with example empirical data from a historical disruption. Results demonstrate that the model effectively balances the three key objectives while adhering to operational constraints, therefore ensuring its practical applicability. The findings highlight the types of train services most beneficial to alter and quantify the improvement achieved, including reductions in service cancellations and compensation-eligible delays. In summary, this research contributes to modelling methods for strengthening the resilience of the rail network by enhancing its ability to recover from disruption efficiently.

ABSTRACT. Hybrid offshore renewable energy systems, which integrate technologies such as wind turbines and floating solar photovoltaics, present promising solutions to enhance the reliability of energy production while reducing investment and operational and maintenance (O&M) costs through shared infrastructure. However, design decisions, such as system sizing, bring critical O&M challenges, including operational accessibility, system reliability, availability, and maintenance strategies that influence the project's economic feasibility and long-term performance. Nevertheless, existing studies often overlook O&M challenges by relying on oversimplified assumptions such as fixed availability rates, maintenance costs as a percentage of overall capital expenditures, or a predetermined number of maintenance over the project's lifespan, neglecting their impact on system sizing decisions. To bridge this gap, this work proposes a novel framework to optimize the system configuration by incorporating a more realistic estimation of O&M costs. The model incorporates design decisions and relevant maintenance strategies, including corrective and preventive approaches, while accounting for component degradation, vessel fleet selection, accessibility, availability, and energy production. When applied to real case studies, this framework provides valuable insights for optimizing the design and operation of hybrid offshore systems. It highlights the interplay between operational and design decisions while maximizing investor returns.

ABSTRACT. This study focuses on the performance analysis of an M/M/1 retrial queueing system that incorporates the dynamics of an unreliable server and customers' impatience. When customers encounter the server in a busy state, they exhibit impatience or decide to join a retrial orbit, where they wait for an opportunity to access the server. After a random period, customers in the orbit may either attempt to retry for service or abandon the system altogether due to impatience. The server is subject to potential breakdowns, reflecting the real-world scenario of service interruptions. To analyze the system, we formulate the Chapman–Kolmogorov equations and solve them using probability-generating functions. These solutions yield the probability distributions, which are then used to derive various performance measures for the system. A numerical example is provided to demonstrate the impact of different system parameters on performance measures. Moreover, the study formulates a profit function based on the customers’ strategy of joining the system. This profit function is optimized using the quasi-Newton method (QNM) to maximize the system's profitability, thereby guiding decision-making to improve overall performance.

ABSTRACT. This paper proposes an advanced simulation-optimization approach to evaluate and optimize the passenger flows within international airports. This approach allocates resources intelligently during the simulation process and balances demand and service quality. The resource allocation performed by our Advanced Resource Management (ARM) algorithm was used to develop an integrated system for arranging resources, identifying the proper resources, and allocating them throughout the model. It was used to investigate the influences of different staff allocation techniques on the inbound and outbound processes of an airport terminal. The purpose of the proposed simulation-optimization approach is to enhance passenger satisfaction through ensuring reasonable wait times during processing at the lowest cost possible (minimal staff hours)