Enhanced Process Fault Diagnosis through Integrating Neural Networks and Andrews Plot

Abstract

With industrial production processes becoming more and more sophisticated, traditional fault diagnosis systems may not achieve reliable diagnosis performance. In order to improve fault diagnosis performance, this paper proposes an enhanced fault diagnosis system by integrating neural networks with Andrews plot. On-line measurements are first processed by Andrews plot and then fed to a neural network for fault classification. Application to a simulated CSTR process indicates that the proposed method can give more reliable and earlier diagnosis than the traditional neural network based fault diagnosis method combined with principal component analysis.