A data-driven recurrent event model for system degradation with imperfect maintenance actions

Abstract

Although a large number of degradation models for industrial systems have been proposed by researchers over the past few decades, the modeling of impacts of maintenance actions has been mostly limited to single-component systems. Among multi-component models, past work either ignores the general impact of maintenance, or is limited to studying failure interactions. In this article, we propose a multivariate imperfect maintenance model that models impacts of maintenance actions across sub-systems while considering continual operation of the unit. Another feature of the proposed model is that the maintenance actions can have any degree of impact on the sub-systems. In other words, we propose a multivariate recurrent event model with stochastic dependence, and for this model we present a two-stage approach which makes estimation scalable, thus practical for large-scale industrial applications. We also derive expressions for the Fisher information so as to conduct asymptotic statistical tests for the maintenance impact parameters. We demonstrate the scalability through numerical studies, and derive insights by applying the model on real-world maintenance records obtained from oil rigs. In the online supplemental material, we provide the following: (i) sketch of proof for likelihood, (ii) convergence analysis, (iii) contamination analysis, and (iv) a set of R codes to implement the current method.

KEYWORDS:

• Recurrent event model
• degradation model
• imperfect maintenance
• multivariate stochastic dependence

Acknowledgments

The authors appreciate the editor and referees for their valuable comments and suggestions.

Funding

The financial support of this work is partially provided by NSF Award No. CMMI-1824761.

Additional information

Notes on contributors

Akash Deep

Akash Deep received the B.Tech. degree in production and industrial engineering from the Indian Institute of Technology Roorkee, Roorkee, India, in 2017. He is currently working toward the Ph.D. degree at the Department of Industrial and Systems Engineering, University of Wisconsin–Madison, Madison, WI, USA. He also has a MS in Statistics from University of Wisconsin–Madison, Madison, WI, USA. His research interests include predictive analytics, survival modeling, and service decision making in Internet-of-Things-enabled smart and connected systems.

Shiyu Zhou

Shiyu Zhou received the B.S. and M.S. degrees in mechanical engineering from the University of Science and Technology of China, Hefei, China, in 1993 and 1996, respectively, and the master’s degree in industrial engineering and the Ph.D. degree in mechanical engineering from the University of Michigan, Ann Arbor, MI, USA, both in 2000.

He is the Vilas Distinguished Achievement Professor with the Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, WI, USA. His research interests include data-driven modeling, monitoring, diagnosis, and prognosis for engineering systems with particular emphasis on manufacturing and after-sales service systems. He has established methods for modeling, analysis, and control of Internet of Things (IoT) enabled smart and connected systems, variation modeling, analysis, and reduction for complex manufacturing processes, and process control methodologies for emerging nanomanufacturing processes. He has won many highly competitive federal research grants. His research also attracted significant interest and funding support from several large corporations.

Dr. Zhou is a recipient of a CAREER Award from the National Science Foundation and the Best Application Paper Award from IIE Transactions. He is currently the Director of the IoT Systems Research Center at the University of Wisconsin-Madison and a Fellow of the IISE, the ASME, and the SME.

Dharmaraj Veeramani

Dharmaraj Veeramani received the B.S. degree in mechanical engineering from the Indian Institute of Technology Madras, Chennai, India, in 1985, and the M.S. and Ph.D. degrees in industrial engineering from Purdue University, West Lafayette, IN, USA, in 1987 and 1991, respectively.

He is the E-Business Chair Professor with the Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, WI, USA. He has received numerous research grants from federal agencies and industry. His research focuses on emerging frontiers of digital business, Internet of Things technologies and applications, smart and connected systems, and supply chain management.

Dr. Veeramani has received multiple honors and awards from organizations, such as the National Science Foundation, the SME, the SAE International, and the ASEE in recognition of his scholarly contributions.