Bayesian framework for fault variable identification

Abstract

In most manufacturing processes, identifying the faulty process variables that may lead to process changes is crucial for quality engineers and practitioners. There are several parametric procedures for identifying faulty variables with the assumption that they follow multivariate normal distributions. However, in practice, the normality assumption restricts the applicability of such procedures in identifying the faulty variables. In addition, conventional procedures for fault identification are often computationally expensive, especially in high-dimensional processes. Therefore, this article proposes a data-driven Bayesian approach for fault identification that addresses the limitations posed by the normality assumption. The proposed approach is computationally efficient for high-dimensional data compared with existing approaches. Experimental results with various simulation studies and real-life data sets demonstrate the effectiveness of the proposed procedure.

KEYWORDS:

• Bayesian statistics
• data-driven
• faulty variable identification
• multivariate statistical process control
• support vector data description

Acknowledgments

The authors appreciate the editors and two anonymous reviewers for their valuable comments and suggestions, which greatly improved this article. The statements made herein are solely the responsibility of the authors.

Additional information

Funding

This article was made possible by the support of NPRP 5-364-2-142 and NPRP 7-1040-2-393 grants from Qatar National Research Fund (QNRF) and NRF-2015R1C1A1A01051487 from the National Research Foundation of Korea.

Notes on contributors

Mehmet Turkoz

Mehmet Turkoz is currently an Assistant Professor of Professional Practice in the Department of Management Science and Information Systems, Rutgers University, USA. He received his PhD from the Department of Industrial and Systems Engineering, Rutgers University, USA in 2018. He holds an MS degree in Operations Research from Rutgers University, USA, in 2012. His research areas include statistical process modeling and monitoring, data mining and operations research.

Sangahn Kim

Sangahn Kim is currently an Assistant Professor in the Department of Quantitative Business Analysis, Siena College, USA. He received his Ph.D. from the Department of Industrial and Systems Engineering, Rutgers University, USA. He is a recipient of the Richard A. Freund International Scholarship by American Society for Quality (ASQ) in 2016. He also won the Best PhD Student Award and the Tayfur Altiok Memorial Scholarship from Rutgers University. His research interests include statistical process modeling and monitoring, stochastic process, reliability engineering, data mining and data analytics.

Young-Seon Jeong

Young-Seon Jeong is an Associate Professor in the Department of Industrial Engineering at Chonnam National University, Gwangju, South Korea. He received his PhD degree in Industrial and Systems Engineering from Rutgers University, USA, in 2011. His current research focuses on data analytics, quality engineering and uncertainty quantification.

Myong K. (MK) Jeong

Myong K. (MK) Jeong is a Professor in the Department of Industrial and Systems Engineering and RUTCOR (Rutgers Center for Operation Research), Rutgers University, New Brunswick, New Jersey. His research interests include data mining, quality and reliability engineering, stochastic processes, and sensor data analysis. He received the prestigious Richard A. Freund International Scholarship by ASQ and the National Science Foundation (NSF) CAREER Award in 2002 and 2007, respectively. His research has been funded by the NSF, United States Department of Agriculture (USDA), National Transportation Research Center, Inc. (NTRCI), and industry. He has been a consultant for Samsung Electronics, Intel, ETRI, and other companies. He has published more than 90 refereed journal articles. He has served as an Associate Editor of several journals such as the IEEE Transaction on Automation Science and Engineering, International Journal of Advanced Manufacturing Technology, and International Journal of Quality, Statistics and Reliability.

Elsayed A. Elsayed

Elsayed A. Elsayed is a Distinguished Professor in the Department of Industrial and Systems Engineering, Rutgers, The State University of New Jersey. His research interests are in the areas of quality and reliability engineering. He is the author of Reliability Engineering, John Wiley & Sons, 2012. He is the author and coauthor of work published in IIE Transactions, IEEE Transactions, and the International Journal of Production Research. His research has been funded by the DoD, FAA, NSF, and industry. Dr. Elsayed has been a consultant for DoD, AT&T Bell Laboratories, Ingersoll-Rand, Johnson & Johnson, Personal Products, AT&T Communications, Ethicon and other companies. Dr. Elsayed was the Editor-in-Chief of IIE Transactions and the Editor of IIE Transactions on Quality and Reliability Engineering. He is also an Editor for the International Journal of Reliability, Quality and Safety Engineering.

Khalifa N. Al-Khalifa

Khalifa N. Al-Khalifa is Full Professor in Industrial Engineering at Qatar University and is currently the President of College of North Atlantic, Qatar. He was awarded his Ph.D. degree in Manufacturing Engineering, University of Birmingham, UK. His research interests focus on Total Quality Management and Quality and Reliability Engineering. He has published over 40 technical publications related to his research interest. Dr. Al-Khalifa is the chair of ASQ Doha-Qatar Local Member Community, and a member of Qatari engineering society. Currently he is managing research funds worth over US$5,000,000. He is also a supervisor to a number of postdoctoral fellow, PhD and masters students.

Abdel Magid Hamouda

Abdel Magid Hamouda is currently the Acting Dean of College of Engineering, Qatar University. He is an active member of a number of International Scientific Committees, professional societies and standards boards. Dr. Hamouda is a fellow of the Royal Society of Art (FRSA), senior member of Institute of Industrial and Systems Engineering (IISE), member of the Institute of Highway Transportation, UK, and member of American Society for Engineering Education (ASEE). Dr. Hamouda has published over 400 articles, of which over 200 are in well-reputed international journals. He has several patents and has edited several conference proceedings. He is currently managing research funds worth over US$4,000,000. He serves on the editorial board of a number of international journals. He and his coworkers have received a number of prestigious awards. Dr. Hamouda was selected by the Organization of Islamic Countries (OIC) as one of the Top 200 scientists within the OIC. In 2010, he was honored with the Takreem Scientific and Technological Achievement Award, one of the highest awards in the Arab world. Also, he won Qatar University Merit Award for the years 2010 and 2014. Recently, he won the Qatar University Research Excellence Award 2016.