Multivariate statistical process control is an essential procedure employed to deliver quality products in modern manufacturing and service industries. Multivariate control charts are an extensively used tool to determine whether a process is performing as intended. Once the control chart detects an abnormal process variable, one difficulty encountered is to interpret the source(s) of the out-of-control signal. Therefore, a novel approach for diagnosing the out-of-control signals in the multivariate process is developed in this paper. The proposed methodology uses the optimized support vector machines (SVM) based on variable-length particle swarm optimization to recognize subclasses of multivariate abnormal process patterns and to identify the responsible variable(s) for their occurrence. Based on a simulation experiment, the proposed approach verifies its capability in accurate classification of the source(s) of out-of-control signal and outperforms the conventional multivariate control scheme based on SVM.
Hotelling’s T2 control chart, Support vector machine, Variable-length particle swarm optimization, Parameter optimization.
Duo XU, Zeshui XU, Shuixia CHEN, "Diagnosing Out-of-Control Signals of Multivariate Control Chart based on Variable Length PSO-SVM", Studies in Informatics and Control, ISSN 1220-1766, vol. 30(3), pp. 5-17, 2021. https://doi.org/10.24846/v30i3y202101
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