Predictive Maintenance (Pilot)

Type of course:

Digital learning, Lesson

Language:

Duration:

13 minutes

Proficiency:

Beginner

Target:

Manager, Professionals

SUMMARY

The Predictive Maintenance lesson in the CTO 5.0 program focuses on advanced maintenance strategies to improve operational efficiency by predicting and preventing equipment failures. Participants will explore forecasting techniques to anticipate machinery issues, optimize maintenance schedules, and minimize downtime, thereby extending asset lifespans.

The session highlights the role of machine learning in predictive maintenance, utilizing historical and real-time data to identify patterns and enhance failure predictions. Additionally, it addresses workload forecasting to optimize resource allocation and maintain production efficiency. By the end, participants will be equipped with knowledge of predictive maintenance principles, forecasting methods, and machine learning applications to implement data-driven strategies effectively.

About The Author

Panos Stavropoulos is an Assistant Professor at the Laboratory for Manufacturing Systems and Automation (LMS), University of Patras, Greece. He has been teaching as a Lecturer (2014-2018) and Assistant Professor (2018-present) on topics of Mechanical Engineering and Manufacturing Processes. He has been coordinating and managing EITM projects and has been involved in nuggets development on topics related to Manufacturing Processes.

Vasiliki Panagiotopoulou is a Senior Research Engineer at the Laboratory for Manufacturing Systems and Automation (LMS) since 2020, with research interests focusing on Sustainability, and Circular Economy. She has extensive experience participating in various EITM education and innovation projects and developing educational content for multiple initiatives, including Demo4Green and SRC4i.

Giorgos Gkoumas has been a Research Engineer at the Laboratory for Manufacturing Systems and Automation (LMS) since 2024. His research interests focus on sustainability and renewable energy technologies. He has experience participating in various projects related to regional development, entrepreneurial discovery, and innovation center development.

Learning outcomes

By the end of this lesson, participants will be able to classify the different methods of forecasting.
By the end of this lesson, participants will be able to assess maintenance techniques that are better suited for their companies.
By the end of this lesson, participants will be able to apply strategies and forecast the maintenance workload of their companies.