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

作者：Sun, Mingze; Qian, Lei; Mukherjee, Amitava; Xiang, Dongdong^*

来源：Quality Technology and Quantitative Management, 2023.

DOI：10.1080/16843703.2023.2244213

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

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.

• 单位
  北京大学