Process monitoring and fault diagnosis method combining bagging DPCA‐ICA with moving window Kolmogorov–Smirnov test

Abstract Process monitoring and fault diagnosis systems are essential for ensuring the quality and safety of industrial processes. Real industrial process data always has dynamic features, autocorrelation features, and non‐Gaussian distributions, making many multivariate statistical process monitoring (MSPM) methods, such as principal component analysis (PCA), not work well. To solve the problem, a new process monitoring method (named DPCA‐ICA‐moving window Kolmogorov–Smirnov test MWKS) is proposed in the paper, which is a combination of DPCA and independent component analysis (ICA) with adaptive weight parameters and introduces a moving window technique and two‐sample K‐S test to obtain the K‐S distance of the commonly used Hotelling's and square predictive error as new monitoring parameters. Experimental results on the Tennessee Eastman chemical process demonstrate the effectiveness and accuracy of the proposed method, which shows it has better performance than existing commonly used methods. The average fault detection rate (FDR) is 93.131%, while the average false alarm rate (FAR) of the proposed method is only 0.1%.