Degradation modeling and monitoring of machines using operation-specific hidden Markov models

Abstract

In this article, a novel data-driven approach to monitoring of systems operating under variable operating conditions is described. The method is based on characterizing the degradation process via a set of operation-specific hidden Markov models (HMMs), whose hidden states represent the unobservable degradation states of the monitored system while its observable symbols represent the sensor readings. Using the HMM framework, modeling, identification, and monitoring methods are detailed that allow one to identify an HMM of degradation for each operation from mixed-operation data and perform operation-specific monitoring of the system. Using a large data set provided by a major manufacturer, the new methods are applied to a semiconductor manufacturing process running multiple operations in a production environment.

Keywords:

• Hidden Markov models
• degradation modeling
• condition monitoring

Appendix. Emission matrices for synthetic problem

For M = 5: For M = 10: For M = 10: For M = 20: For M = 25: For M = 30:

Additional information

Notes on contributors

Michael E. Cholette

Michael Cholette received his B.S. in Mechanical Engineering from the University of Michigan, Ann Arbor, in 2007 and his M.S. and Ph.D. from the University of Texas at Austin in August 2012 in the area of dynamic systems and control. Between 2009 and 2012 he was a Research Assistant at the University of Texas at Austin, where he worked on a number of projects in modeling and monitoring of dynamic systems, particularly those whose operating conditions vary. Between 2009 and 2011 this research was supported by the International SEMATECH Manufacturing Initiative. He is currently a lecturer at the Queensland University of Technology in Brisbane, Queensland, Australia. His research interests include fault detection, diagnosis, prognosis, and control for complex systems.

Dragan Djurdjanovic

Dragan Djurdjanovic obtained his B.S. in Mechanical Engineering and in Applied Mathematics in 1997 from the University of Nis, Serbia, his M.Eng. in Mechanical Engineering from the Nanyang Technological University, Singapore, in 1999, and his M.S. in Electrical Engineering (Systems) and Ph.D. in Mechanical Engineering in 2002 from the University of Michigan, Ann Arbor. His research interests include advanced quality control in multistage manufacturing systems, advanced diagnostics and maintenance decision making, as well as applications of advanced signal processing in biomedical engineering. He has co-authored 47 published or accepted journal publications, three book chapters, and 32 conference publications. He is the recipient of several prizes and awards, including the 2006 Outstanding Young Manufacturing Engineer Award from the Society of Manufacturing Engineers, 2005 Teaching Incentive Award from the Department of Mechanical Engineering of the University of Michigan, Nomination for the Distinguished Ph.D. Thesis from the Department of Mechanical Engineering, University of Michigan in 2003, and The Outstanding Paper Award at the 2001 SME North American Manufacturing Research Conference.