Fault diagnosis method for chemical processes based on variable correlation‐guided convolutional neural networks

Abstract Convolutional neural networks (CNNs) have been widely applied in chemical process fault diagnosis due to their superior feature extraction capabilities. However, the inherent complexity and variability of chemical environments, involving multivariable interactions, noise interference, and other factors, pose challenges that hinder the direct application of CNNs. These limitations may compromise the accuracy of fault diagnosis by hindering the full exploitation of CNNs' feature extraction capabilities. To address these challenges, this paper proposes a novel fault diagnosis method for chemical processes based on a variable correlation‐guided CNN. The proposed method uses the Pearson correlation coefficient to identify strongly correlated variable groups, integrating them into the original variables. This integration facilitates the convolution of these strongly correlated variables, thereby enhancing the extraction of more discriminative features and optimizing fault diagnosis methods. This approach enables CNNs to more accurately extract fault‐relevant features, thereby improving diagnostic performance. The effectiveness of the proposed method is validated through comprehensive numerical simulations and the Tennessee Eastman (TE) process dataset. The results demonstrate substantial enhancements in both the accuracy and reliability of fault detection, validating the superiority of the proposed method.