Multimode Process Monitoring Based on Sparse Principal Component Selection and Bayesian Inference-Based Probability

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According to the demand for diversified products, modern industrial processes typically have multiple operating modes. At the same time, variables within the same mode often follow a mixture of Gaussian distributions. In this paper, a novel algorithm based on sparse principal component selection (SPCS) and Bayesian inference-based probability (BIP) is proposed for multimode process monitoring. SPCS can be formulated as a just-in-time regression between all PCs and each sample. SPCS selects PCs according to the nonzero regression coefficients which indicate the compact expression of the sample. This expression is necessarilydiscriminative: amongst all subset of PCs, SPCS selects the PCs which most compactly express the sample and rejects all other possible but less compact expressions. BIP is utilized to compute the posterior probabilities of each monitored sample belonging to the multiple components and derive an integrated global probabilistic index for fault detection of multimode processes. Finally, to verify its superiority, the SPCS-BIP algorithm is applied to the Tennessee Eastman (TE) benchmark process and a continuous stirred-tank reactor (CSTR) process.