ISE seminar title

Adaptable and generic methods for monitoring, prognostics and maintenance planning of energy assets

Speaker

Mohammad Badfar, Ph.D. Student, Wayne State University

Abstract

Effective monitoring and prognostics are crucial for maintaining the reliability and efficiency of energy assets, enabling early issue detection and optimized maintenance schedules. Real-world challenges like poor data quality, sparse data, environmental factors, and diverse operating conditions necessitate adaptable solutions. This seminar introduces scalable, data-driven approaches to these challenges, focusing on developing generic frameworks for monitoring and prognostics across various energy systems. Leveraging advanced analytics and modeling techniques, we demonstrate how to effectively handle data quality issues and environmental influences. Outcomes include robust monitoring systems that enhance fault detection and maintenance planning. The seminar also explores applying these scalable solutions to real-world scenarios, contributing to advancements in energy asset management and monitoring methodologies.

Bio

Mohammad Badfar is a Ph.D. student in the Department of Industrial and Systems Engineering at Wayne State University. His research interests include adaptable solutions for monitoring, diagnostics, and maintenance planning of energy assets. Mohammad Badfar received his M.S. in Industrial and Systems Engineering from the Sharif University of Technology and his B.S. in Industrial and Systems Engineering from the University of Tehran.