An EWMA chart for high dimensional process with multi-class out-of-control information via random forest learning

ABSTRACT

Modern manufacturing and quality monitoring involve multi-class out-of-control (OOC) information from the training sample. It is essential to use such information during online monitoring of data streams from complex processes. In this paper, a monitoring framework is designed by combining the random forest technique with the exponentially weighted moving average method for monitoring complex processes with multi-class OOC information. To be specific, a process surveillance technique in the form of a control chart is proposed based on the probability that the online data is classified as an in-control (IC) sample, and the control chart triggers an alarm when the probability is lower than the control limit. Our numerical findings based on the Monte–Carlo simulation show that the proposed control chart performs more effectively than its competitors under various distributions and data types, especially for high-dimensional cases when multi-class OOC information is known in advance. Moreover, the proposed method is illustrated with an application using the data related to the hard disk manufacturing processes.

KEYWORDS:

• Complex process
• MEWMA
• random forest
• statistical process control

Acknowledgements

The authors sincerely acknowledge the efforts of the Editor, the Associate Editor and two anonymous referees that have resulted in significant improments of this paper. This work was supported by the National Key R&D Program of China [2021YFA1000101; 2021YFA1000102; 2022YFA1003801], National Natural Science Foundation of China [12071144; 71931004; 11771145], Basic Research Project of Shanghai Science and Technology Commission (22JC1400800).

Disclosure statement

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

Additional information

Notes on contributors

Mingze Sun

Mingze Sun is a master's student at Tsinghua University. He received a B.S. degree under the supervision of Professor Dongdong Xiang from East China Normal University, Shanghai, China, in 2022.

Lei Qian

Lei Qian is a postgraduate candidate in Peking University. He received B.S. in statistics under the supervision of Professor Dongdong Xiang from East China Normal University, Shanghai, China, in 2023.

Amitava Mukherjee

Amitava Mukherjee, Professor of Production, Operations and Decision Sciences Area, XLRI-Xavier School of Management, Jamshedpur, India. His main research interest is Statistical Process Control.

Dongdong Xiang

Dongdong Xiang, Professor of School of statistics at East China Normal University. His main research interests are Statistical Process Control, Large-scale Multiple Tests and Machine Learning.