Variable selection methods in multivariate statistical process control: A systematic literature review. (January 2018)

• Record Type: Journal Article
• Authors: Peres, Fernanda Araujo Pimentel; Fogliatto, Flavio Sanson
• Is Part Of: Computers & industrial engineering. Volume 115(2018)
• Page Start: 603
• Page End: 619
• Publication Date: 2018-01
• Subjects: Variable selection -- Multivariate statistical process control -- Industrial process monitoring -- High dimensional dataset; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2017.12.006
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: Limitations of multivariate statistical process control methods are analyzed. The integration of variable selection into MSPC methods is thoroughly investigated. Thirty methods published between 2002 and 2017 are covered in this review. Methods are categorized according objectives and process monitoring steps. Five classes of future research opportunities are presented. Abstract: Technological advances led to increasingly larger industrial quality-related datasets calling for process monitoring methods able to handle them. In such context, the application of variable selection (VS) in quality control methods emerges as a promising research topic. This review aims at presenting the current state-of-the-art of the integration of VS in multivariate statistical process control (MSPC) methods. Proposals aligned with the objective were identified, classified according to VS approach, and briefly presented. Research on the topic has considerably increased in the past five years. Thirty methods were identified and categorized in 10 clusters, according to the objective of improvement in MSPC and the step of process monitoring they were aimed to improve. The majority of the propositions were either targeted at exclusively monitoring potential out-of-control variables or improving the monitoring of in-control variables. MSPC improvements were centered in principal component analysis (PCA) projection methods, while VS was mainly carried out using the Least Absolute Shrinkage and Selection Operator (LASSO) method and genetic algorithms. Fault isolation was the most addressed step in process monitoring. We close the paper proposing five topics for future research, exploring the opportunities identified in the literature.