GMM and optimal principal components-based Bayesian method for multimode fault diagnosis |
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Affiliation: | 1. State Key Laboratory of Industrial Control Technology, Institute of Industrial Process Control, Department of Control Science and Engineering, Zhejiang University, Hangzhou 310027, Zhejiang, P. R. China;2. Key Laboratory of Advanced Control and Optimization for Chemical Processes, Shanghai, 200237, P.R. China;1. School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan, Shandong, 250101, China;2. College of Information and Control Engineering, China University of Petroleum (East China), Qingdao, Shangdong 266580, China |
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Abstract: | Principal component analysis (PCA) serves as the most fundamental technique in multivariate statistical process monitoring. However, other than determining contributions to a fault from each variable based on the pre-selected major principal components (PCs), the PCA-based fault diagnosis with an optimal selection of PCs is seldom investigated. This paper presents a novel Gaussian mixture model (GMM) and optimal principal components (OPCs)-based Bayesian method for efficient multimode fault diagnosis. First, the GMM and Bayesian inference is utilized to identify the operating mode, and then local PCA model is established in each mode. Second, given that the various principal components (PCs) may contain distinct fault signatures, the behavior of each PC in local PCA is examined and the OPCs are selected through stochastic optimization algorithm. Based on the OPCs, a Bayesian diagnosis system is then formulated to identify the fault statuses in a probability manner. Performance of GMM–OPC Bayesian diagnosis is examined through a numerical example and the Tennessee Eastman challenge process. The efficiency and feasibility are demonstrated. |
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Keywords: | Gaussian mixture model Principal component analysis Bayesian method Process monitoring Fault diagnosis |
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