Digitalization of the Industrial Drivetrain to Optimize Energy Consumption and Efficiency

EasyChair Preprint 14615

Abstract

Detecting upcoming machine outages as well as energy saving potentials combines two of the main use cases of companies’ digital transformation: predictive maintenance and energy optimization. By implementing sensor-based as well as sensor-less digital solutions for drive trains and machines, which are addressing both use cases in industrial environments, operators are not only achieving a fast return on invest but also ensure continuous transparency and improvement of their equipment, resources, and processes. Providing the flexibility to run the different software solutions either on premise or in the cloud enables a seamless integration into the different IT ecosystems and is an important aspect of IT/OT integration.

Keyphrases: Cloud, IT/OT integration, Predictive Maintenance, Productivity, cloud-based, condition monitoring, digital drivetrain value chain, digitalization of electrical drivetrains, drivetrain analyzer cloud, electric motor driven systems, electric motors, energy consumption, energy efficiency, energy saving, industrial drivetrain, on-premise, plug and play, system efficiency, transparency, uptime

@booklet{EasyChair:14615,
  author    = {Matthias Manger and Jonas Harant},
  title     = {Digitalization of the Industrial Drivetrain to Optimize Energy Consumption and Efficiency},
  howpublished = {EasyChair Preprint 14615},
  year      = {EasyChair, 2024}}