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

Lookup NU author(s): Dr Shengkai Wang, Dr Jie ZhangORCiD



This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


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.

Publication metadata

Author(s): Wang S, Zhang J

Publication type: Article

Publication status: Published

Journal: Processes

Year: 2021

Volume: 9

Issue: 9

Online publication date: 14/09/2021

Acceptance date: 09/09/2021

Date deposited: 15/09/2021

ISSN (electronic): 2227-9717

Publisher: MDPI


DOI: 10.3390/pr9091659


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