A variable‐selection control chart via penalized likelihood and Gaussian mixture model for multimodal and high‐dimensional processes. (18th February 2019)

• Record Type: Journal Article
• Authors: Yan, Dandan; Zhang, Shuai; Jung, Uk
• Is Part Of: Quality and reliability engineering international. Volume 35:Number 4(2019)
• Page Start: 1263
• Page End: 1275
• Publication Date: 2019-02-18
• Subjects: Gaussian mixture model -- high dimensionality -- multimodality -- penalized likelihood -- statistical process control -- variable selection; Reliability (Engineering) -- Periodicals; Quality control -- Periodicals; High technology -- Periodicals; 620.00452
• DOI: 10.1002/qre.2458
• Languages: English
• ISSNs: 0748-8017

Abstract

Abstract: With the development of the sensor network and manufacturing technology, multivariate processes face a new challenge of high‐dimensional data. However, traditional statistical methods based on small‐ or medium‐sized samples such as T 2 monitoring statistics may not be suitable because of the "curse of dimensionality" problem. To overcome this shortcoming, some control charts based on the variable‐selection (VS) algorithms using penalized likelihood have been suggested for process monitoring and fault diagnosis. Although there has been much effort to improve VS‐based control charts, there is usually a common distributional assumption that in‐control observations should follow a single multivariate Gaussian distribution. However, in current manufacturing processes, processes can have multimodal properties. To handle the high‐dimensionality and multimodality, in this study, a VS‐based control chart with a Gaussian mixture model (GMM) is proposed. We extend the VS‐based control chart framework to the process with multimodal distributions, so that the high‐dimensionality and multimodal information in the process can be better considered.