Lei Qian

2023

1. QTQM

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

   Mingze Sun, Lei Qian, Amitava Mukherjee, and Dongdong Xiang

   Quality Technology & Quantitative Management, 2023

   Abs Bib HTML PDF

   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.

   @article{doi:10.1080/16843703.2023.2244213,
     author = {Sun, Mingze and Qian, Lei and Mukherjee, Amitava and Xiang, Dongdong},
     title = {An EWMA chart for high dimensional process with multi-class out-of-control information via random forest learning},
     journal = {Quality Technology \& Quantitative Management},
     volume = {0},
     number = {0},
     pages = {1-27},
     year = {2023},
     publisher = {Taylor & Francis},
     doi = {10.1080/16843703.2023.2244213},
     url = { https://doi.org/10.1080/16843703.2023.2244213},
     eprint = {https://doi.org/10.1080/16843703.2023.2244213},
   }