Fault Detection and Identification with Kernel Principal Component Analysis and Long Short-Term Memory Artificial Neural Network Combined Method

A new fault detection and identification approach is proposed. The kernel principal component analysis (KPCA) is first applied to the data for reducing dimensionality, and the occurrence of faults is determined by means of two statistical indices, T-2 and Q. The K-means clustering algorithm is then adopted to analyze the data and perform clustering, according to the type of fault. Finally, the type of fault is determined using a long short-term memory (LSTM) neural network. The performance of the proposed technique is compared with the principal component analysis (PCA) method in early detecting malfunctions on a continuous stirred tank reactor (CSTR) system. Up to 10 sensor faults and other system degradation conditions are considered. The performance of the LSTM neural network is compared with three other machine learning techniques, namely the support vector machine (SVM), K-nearest neighbors (KNN) algorithm, and decision trees, in determining the type of fault. The results indicate the superior performance of the suggested methodology in both early fault detection and fault identification.

Automated summary

A new fault detection and identification approach is proposed, which involves the application of KPCA for dimensionality reduction, statistical indices for fault occurrence determination, K-means clustering for data analysis and clustering, and LSTM neural network for fault type determination. The proposed technique is compared with PCA method and three other machine learning techniques, namely SVM, KNN, and decision trees. The results demonstrate the superior performance of the suggested methodology in both early fault detection and fault identification.