Online Process Fault Diagnosis through Integration of Principal Component Analysis, Modified Recurrence Plot, and Convolutional Neural Networks

This paper presents an effective online process fault diagnosis method by integrating recurrence plots (RP) with convolutional neural networks (CNN). To cope with the high dimension of process data, principal component analysis (PCA) is applied to the original process data. RPs are then produced using the major principal components (PCs). As RPs are symmetric, this paper proposes to merge the two RPs for the first and second PCs into one to represent more information. The merged RPs serve as the inputs to a CNN, which is trained for fault diagnosis. The proposed fault diagnosis method is demonstrated on a simulated continuous stirred tank reactor (CSTR) system. It is shown that the proposed fault diagnosis system gives enhanced diagnosis performance.