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ABSTRACT
Conventional statistical process control tools monitor either continuous or count data but rarely both simultaneously. While process data are becoming increasingly complex, there will be more data points containing both continuous and count information. In the case of mixed continuous and count data with unknown distributions, the traditional parameter control chart cannot be used to monitor them. It is proposed in this paper a novel nonparametric EWMA control chart to monitor mixed continuous and count data. The mixed continuous and count data are first transformed into categorical data, and then a log-linear model is utilized to analyze correlations between variables, followed by the construction of an EWMA statistic that is used to monitor mixed continuous and count data. Next, the proposed control chart is compared with several improved control charts for monitoring mixed continuous and count data. Based on the numerical simulation results, the control chart presented in this paper provides a superior method of detecting alarm signals in the process compared to some improved control charts. Finally, the proposed control chart is demonstrated to be effective and applicable using the semiconductor manufacturing process dataset from the UC Irvine Machine Learning Repository.
Acknowledgement
The authors are very grateful to the editor and reviewers for their valuable and constructive comments and suggestions, which greatly improved the final version of this paper.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Correction Statement
This article has been corrected with minor changes. These changes do not impact the academic content of the article.
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Notes on contributors
Li Xue
Li Xue is a full Professor and Associate Dean of the School of Management Engineering at the Zhengzhou University of Aeronautics, China. Her major research areas include quality control and management, and big data analytics. She has published over 40 papers in these areas, and her research has been well supported by grants from the National Science Foundation of China, Aeronautic Science Foundation of China, and other grant agencies.
Qiuyu Wang
Qiuyu Wang is a master's student of the School of Management Engineering at the Zhengzhou University of Aeronautics, China, currently studying in the School of Management Engineering. Her major research areas include quality control and management.
Zhen He
Zhen He is a full professor in the College of Management and Economics at Tianjin University, China. He received his PhD degree from Tianjin University in 2000. His research interests include quality engineering, six sigma, industrial engineering, and operations management. So far, he has published over 100 research papers, many of which appeared in top journals.
Peihua Qiu
Peihua Qiu is Professor and Founding Chair of the Department of Biostatistics at the University of Florida. He is an elected fellow of the American Statistical Association, an elected fellow of the Institute of Mathematical Statistics, and an elected member of the International Statistical Institute. He was the editor of Technometrics and served as associate editor for a number of top journals, including the Journal of the American Statistical Association, Biometrics, and Technometrics. He has made substantial contributions in several research areas, including jump regression analysis, image processing, statistical process control, survival analysis, and disease screening and surveillance. So far, he has published over 125 research papers, many of which appeared in top journals.