An Intelligent Process Fault Diagnosis System based on Andrews Plot and Convolutional Neural Network
Shengkai Wang, Jie Zhang
Abstract
This paper proposes an intelligent process fault diagnosis system based on the techniques of Andrews plot and convolutional neural network. The proposed fault diagnosis method extracts features from the on-line process measurements using Andrews function. To address the uncertainty of setting the proper dimension of extracted features in Andrews function, a convolutional neural network is used to further extract diagnostic information from the Andrews function outputs. The outputs of the convolutional neural network are then fed to a single hidden layer neural network to obtain the final fault diagnosis result. The proposed fault diagnosis system is compared with a conventional neural network based fault diagnosis system. Applications to a simulated CSTR process show that the proposed fault diagnosis system gives much better performance than the conventional neural network based fault diagnosis system. It reveals that the use of Andrews function and convolutional neural network can improve the diagnosis performance.