An Intelligent Process Fault Diagnosis System Based on Neural Networks and Andrews Plot

Shengkai Wang,Jie Zhang

doi:10.3390/pr9091659

Abstract

This paper proposes a neural network-based process fault diagnosis system with Andrews plot for information pre-processing to enhance the performance of online process fault diagnosis. By using features extracted from Andrews plot as the inputs to a neural network, as a classifier, the diagnosis speed and reliability are improved. A method for determining the important features in the Andrews function is proposed. The proposed fault diagnosis system is applied to a simulated continuous stirred tank reactor process and is compared with two conventional neural network-based fault diagnosis systems: scheme B where the monitored measurements are directly fed to a neural network after scaling and scheme C where the monitored measurements are converted to qualitative trend data before feeding to a neural network. Of all the considered faults, the proposed fault diagnosis system diagnosed the abrupt faults on average 5.45 s and 2.66 s earlier than schemes B and C, respectively and diagnosed the incipient faults on average 13.82 s and 5.09 s earlier than schemes B and C, respectively. The results reveal that Andrews plot method utilized in online process monitoring has a great potential in industrial process monitoring.

Highlights

The breakthroughs and advances in industrial technology have made modern industrial production processes more automatic and productive with complex operational functionalities
This paper aims to propose a novel fault diagnosis system to achieve a positive promotion in industrial process monitoring
This paper proposes an enhanced intelligent neural network based online process fault diagnosis system by integrating Andrews plot and neural network techniques

Summary

Introduction

The breakthroughs and advances in industrial technology have made modern industrial production processes more automatic and productive with complex operational functionalities. These improvements have enhanced the product quality and expanded the production scale during the past decades. Authoritativeness of local authorities may be challenged due to the duty of environmental conservation and enterprises may risk heavy penalties Disastrous consequences, such as casualties, usually bring inestimable lost, which should be completely avoided. With the advancement of industrial technology, the improvement of social environmental awareness and the increased demand for high-quality products, the importance of industrial process monitoring is becoming increasingly important. This paper aims to propose a novel fault diagnosis system to achieve a positive promotion in industrial process monitoring

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