Self-organized criticality in manufacturing system unscheduled downtime series and its application to major failure prediction

Abstract

In this study, self-organized criticality theory was applied to analyze the unscheduled downtime (USDT) characteristic of a complex manufacturing system. The long-range correlation of the USDT series was verified through the Hurst exponent value. Then, we developed a model to depict the relationship between the frequency and USDT duration with the extreme value theory. The parameters of the model were estimated with the maximum likelihood method and updated with Bayesian inference when new data was available. Finally, the effectiveness of the proposed method was validated by the USDT data of the bottleneck system from a semiconductor assembly and test factory.

Keywords:

• Bayesian inference
• extreme value theory
• major failure prediction
• self-organized criticality
• unscheduled downtime

Additional information

Funding

This work is sponsored by the Technology Support Plan of Sichuan province under Grant No. 2022ZDZX0002 and 2022ZDZX0037. We also acknowledge support from Intel Products (Chengdu) Co. Ltd. which provided such a good environment for the research.

Notes on contributors

Bo Li

Bo Li was born in 1975. He received his B.S. degree in mechanical engineering from the Nanchang Institute of Aeronautic Technology, Nanchang, China in 1997 and his M.S. degree in mechanical engineering from Guizhou University of Technology, Guiyang, China in 2000 and his Ph.D. degree in mechanical engineering from Zhejiang University, Hangzhou, China in 2003. He is now a professor in the University of Electronic Science and Technology of China (UESTC), Chengdu, China. His research interests include production planning and control, fault prediction and diagnosis and maintenance, and system integrated and automation.

Hengchang Liu

Hengchang Liu received the B.S. degree in Electrical engineering and its automation from Suzhou University, Anhui, China, in 2018, the M.S. degree in mechanical engineering from Beijing University of Civil Engineering and Architecture, Beijing, China, in 2021. He is currently pursuing the Ph.D. degree with the School of Astronautics and Aeronautics, University of Electronic Science and Technology of China, Chengdu, China. His current research interests include maintenance optimization and fault diagnosis.

Feng Yang

Feng Yang was born in 1986. She received her B.S. degree in electronic engineering from the University of Electronic Science and Technology of China, Chengdu, China in 2009 and her M.S. degree in system engineering from the same university in 2012. Her research interests include fault prediction, and fault diagnosis and maintenance.

Nan Jiang

Nan Jiang received the B.S. from Shandong University in Shandong, China in 2020, she is currently pursuing the M.S. degree with the School of Astronautics and Aeronautics, University of Electronic Science and Technology of China, Chengdu, China. Her research interests include industrial intelligent manufacturing, fault prediction.