Real-time profile monitoring schemes considering covariates using Gaussian process via sensor data

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.

KEYWORDS:

• Nonparametric profile monitoring
• covariate information
• Gaussian process
• control chart
• within-profile correlation
• busbar state monitoring

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the [National Natural Science Foundation of China] under Grant [numbers 72032005, 71661147003].

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.