ABSTRACT
Profile monitoring faces great challenges on account of the rapid development of advanced sensor technology. Massive sensor data are highly correlated and change in a complex way over time, which are difficult to describe with parametric models. Furthermore, quality characteristics are often affected by covariates. In this paper, nonparametric monitoring schemes considering covariates are proposed to monitor the correlated profiles in real-time. A profile model considering covariates based on Gaussian process is developed to predict the expected profile. Two control charts are then constructed based on the differences between the observed and expected profiles, which are calculated by Euclidean distance and definite integral, respectively. The effectiveness of the proposed monitoring schemes is validated by simulations. The proposed schemes are applied to a real case of busbar state monitoring in an automotive manufacturing plant.
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Notes on contributors
Ning Ding
Ning Ding is a Ph.D. candidate in the College of Management and Economics at Tianjin University, China. She received her B.S. degree in industrial engineering from Tianjin University, China. Her research interests include quality management, statistical process control, profile monitoring.
Zhen He
Zhen He is a Professor in College of Management and Economics, Tianjin University, China. He is the head of the Department of Industrial Engineering at Tianjin University. He is also the Area Editor of the Computers & Industrial Engineering (CIE). He is an Academician of the International Academy for Quality (IAQ). He received both his Ph.D. and M.S. degree in industrial engineering from Tianjin University. He has authored over 200 refereed journal publications. His research interests include quality engineering and Six Sigma.
Shuguang He
Shuguang He is a Professor in College of Management and Economics, Tianjin University, China. He received his Ph.D. degree in management science and engineering from Tianjin University, China. He has published more than 50 papers in research journals, such as International Journal of Production Research, Journal of Quality Technology, Reliability Engineering & System Safety, Annals of Operations Research, and International Transactions in Operations Research. His research interests include quality management, warranty data analysis, and statistical quality control.
Lisha Song
Lisha Song is an Assistant Professor in College of Science, North China University of Technology, China. She received her Ph.D. degree in business administration from Tianjin University, China. She received her M.S. degree in statistics from Nanjing Normal University, China. Her research interests include statistical process control and profile monitoring.