Maintenance optimization for complex systems is an increasing critical issue in manufacturing industries including automobiles and semiconductors. Using IoT and smart censors, engineers aim to decide proper maintenance time points or intervals via health indicators representing system conditions. In this seminar, I introduce prognostic health management (PHM) and predictive maintenance (PdM) via off-line and on-line data for complex systems. Using off-line data, I present statistical models (e.g., nonhomogeneous Poisson process (NHPP), frailty models) for repairable systems. For PHM, I introduce a general five-stage process for PHM and PdM. I also present condition based maintenance policy using signal processing and statistical process control techniques, based on on-line sensor data. Finally, I present several real case studies for PHM and PdM in power plants and automobiles.

Dr. Suk Joo Bae is a Professor of the Department of Industrial Engineering at Hanyang University, Seoul, Korea. Prof. Bae received his Ph.D. from the School of Industrial & Systems Engineering at Georgia Institute of Technology, in 2003. Prof. Bae was the President of Korean Society of Prognostics & Health Management in 2023 and Korea Reliability Society in 2025. Prof. Bae was the Editor-in-Chief of the Journal of the Korean Society for Quality Management, The Journal of Applied Reliability, and the Associate Editor of IEEE Transactions on Reliability, IISE Transactions, and Informs Journal on Data Science. Prof. Bae has published more than 100 journal papers including Technometrics, Journal of Quality Technology, IISE Transactions, Reliability Engineering & System Safety, and IEEE Transactions on Reliability. His research interests include accelerated life & degradation tests, condition-based maintenance, health monitoring, and remaining useful life prediction for batteries and fuel cells.