A novel sparse Bayesian learning and its application to fault diagnosis for multistation assembly systems

Abstract

This article addresses the problem of fault diagnosis in multistation assembly systems. Fault diagnosis is to identify process faults that cause excessive dimensional variation of the product using dimensional measurements. For such problems, the challenge is solving an underdetermined system caused by a common phenomenon in practice; namely, the number of measurements is less than that of the process errors. To address this challenge, this article attempts to solve the following two problems: (i) how to utilize the temporal correlation in the time series data of each process error and (ii) how to apply prior knowledge regarding which process errors are more likely to be process faults. A novel sparse Bayesian learning method is proposed to achieve the above objectives. The method consists of three hierarchical layers. The first layer has parameterized prior distribution that exploits the temporal correlation of each process error. Furthermore, the second and third layers achieve the prior distribution representing the prior knowledge of process faults. Since posterior distributions of process faults are intractable, this article derives approximate posterior distributions via Variational Bayes inference. Numerical and simulation case studies using an actual autobody assembly process are performed to demonstrate the effectiveness of the proposed method.

Keywords:

• Fault diagnosis in multistation assembly systems
• sparse Bayesian learning
• temporal correlation in process error
• prior knowledge of process faults
• variational Bayes inference

Data availability statement

The authors confirm that the data supporting the findings of this study are available within the article and its supplementary materials.

Additional information

Funding

This project was funded by the following grant from the Department of Defense: N00014-19-1-2728.

Notes on contributors

Jihoon Chung

Jihoon Chung received his BS degree in industrial engineering from Hanyang University, Seoul, Korea, in 2015. He obtained his MS degree in industrial and systems engineering at the Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea, in 2017. He is currently pursuing a PhD degree in industrial and systems engineering from Virginia Tech, Blacksburg, VA, USA. His research interests include statistical learning and data analytics in smart manufacturing.

Bo Shen

Bo Shen is an assistant professor in the Department of Mechanical and Industrial Engineering at the New Jersey Institute of Technology. He received his PhD in industrial and systems engineering at Virginia Tech, Blacksburg, VA, in August 2022. He also received his BS degree in statistics from the University of Science and Technology of China, Hefei, China, in July 2017. His research interests include optimization and machine learning, and data analytics in smart manufacturing.

Zhenyu (James) Kong

Zhenyu (James) Kong received his BS and MS degrees in mechanical engineering from Harbin Institute of Technology, China, in 1993 and 1995, respectively, and his PhD degree from the Department of Industrial and System Engineering, University of Wisconsin Madison, Madison, WI, USA, in 2004. He is currently a professor with the Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA, USA. His research interests include sensing and analytics for smart manufacturing, and modeling, synthesis, and diagnosis for large and complex manufacturing systems. He is a fellow of the Institute of Industrial and Systems Engineers and the American Society of Mechanical Engineers. He was recognized as one of the 20 Most Influential Academics in Smart Manufacturing by the Society of Manufacturing Engineering in 2021.