Effective Predictive Maintenance at the Intersection of Machine Learning and Operations Research

Thursday Mar 5, 2026, 3:00 PM → 4:00 PM Europe/Amsterdam

Nikolaus Haselgruber (CIS Consulting in Industrial Statistics GmbH)

Effective Predictive Maintenance at the Intersection of Machine Learning
and Operations Research

Speaker: Dr. David W. Coit (Rutgers University, Piscataway, NJ, USA)

Chair: Nikolaus Haselgruber

Date: 5th March 2026, at 15:00-16:00 CET

Abstract:

The most effective and useful application of predictive maintenance requires both operations research models and machine learning or AI working together. Historically, development of maintenance planning models has involved the rigorous application of reliability and maintenance probabilistic models and theory combined with formal analytical tools associated with operations research and mathematical programming. These models, while effective, tend to produce static and non-changing strategies that do not reflect changing usage and environment conditions. Furthermore, they are based on population characteristics and do not adequatelyreflect individual differences of units within the population or system. Alternatively, predictive maintenance models which embraced machine learning can dynamically predict a remaining useful life or RUL. These models do specifically reflect individual units within the system andcan also adapt to changing conditions. However, their true effectiveness also requires a meaningful decision-rule on when to take action and what action, i.e., either replace or repair or dynamically reduce work-load to compensate for anticipated degradation. To be truly effective, a combination of these philosophies is needed. The usefulness of predictive maintenance decision rules require the three-way integration or intersection of machine learning, reliability/maintenance theory and operations research. In this talk, we will summarize different approaches to preventive or predictive maintenance models, discuss their relative advantages and disadvantages, highlight a few notable examples to demonstrate this three-way integration, and finally present future research challenges.

Bio:

David Coit is a Professor in the Department of Industrial & Systems Engineering at Rutgers University, Piscataway, NJ, USA. He has also had visiting professor positions at Universite Paris-Saclay, Paris, France and Tsinghua University, Beijing, China. His current teaching and research involves system reliability modeling and optimization, and energy systems optimization. He has over 140 published journal papers and over 100 peer-reviewed conference papers (h-index 70), including the most highly cited paper ever in Reliability Engineering & System Safety (RESS) and the 4th most cited paper in IEEE Transactions on Reliability. He is currently an Associate Editor for RESS and Journal of Risk and Reliability and was previously an Associate or Department Editor for IEEE Transactions on Reliability and IISE Transactions. His research has been funded by USA National Science Foundation (NSF), including a NSF CAREER grant to develop new reliability optimization algorithms considering uncertainty. He has been the recipient of the P. K. McElroy award, Alain O. Plait award and William A. J. Golomski award for best papers and tutorials at the Reliability and Maintainability Symposium (RAMS). Prof. Coit received a BS degree in mechanical engineering from Cornell University, an MBA from Rensselaer Polytechnic Institute (RPI), and MS and PhD in industrial engineering from the University of Pittsburgh. He is a fellow of the Institute of Industrial & Systems Engineers (IISE).

3:00 PM → 4:00 PM Effective Predictive Maintenance at the Intersection of Machine Learning and Operations Research

Speaker: David Coit (Rutgers University)