Improved Process Fault Diagnosis by Using Neural Networks with Andrews Plot and Autoencoder

Abstract

With industrial production processes becoming more and more sophisticated, traditional fault diagnosis systems may be insufficient to meet current industrial diagnostic performance requirements. In order to improve fault diagnosis performance, this paper proposes an enhanced neural network based fault diagnosis system by integrating Andrews plot and Autoencoder. Features are first extracted from on-line measurements by Andrews plot and the high-dimensional features are compressed by autoencoder to an appropriate dimension, which are then fed to a neural network for fault classification. Application to a simulated CSTR process demonstrates that the proposed method can give more reliable and earlier diagnosis than the traditional neural network based fault diagnosis method.