Fault Detection of Nonlinear Processes Using Fuzzy C-means-based Kernel PCA

Nonlinearity in industrial processes such as chemical and biological processes is still a significant problem. Kernel principal component analysis (KPCA) has recently proven to be a powerful tool for monitoring nonlinear processes with numerous mutually correlated measured variables. One of the drawbacks of original KPCA is that computation time increases with the number of samples. In this article, fuzzy C-means clustering technique (FCM) is adopted to reduce the computational complexity of KPCA. The proposed approach (FCM-KPCA) is applied for fault detection of the Tennessee Eastman chemical process. Simulation results show the effectiveness of the proposed approach in terms of low computational cost and low missed detection rate. Keywords—Fault detection, fuzzy C-means, kernel PCA, nonlinear processes.

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