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

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

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

Abstract

This paper proposes an intelligent process fault diagnosis system through integrating 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 and integrating Andrews function with neural network and manual selection of features in Andrews function outputs. 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 and manual selection of features in Andrews function outputs. It reveals that the use of Andrews function and convolutional neural network can improve the diagnosis performance.

Publication metadata

Author(s): Wang S, Zhang J

Publication type: Article

Publication status: Published

Journal: Journal of Dynamics, Monitoring and Diagnostics

Year: 2022

Volume: 1

Issue: 3

Pages: 127-138

Online publication date: 19/05/2022

Acceptance date: 13/05/2022

Date deposited: 18/05/2022

ISSN (electronic): 2831-5308

Publisher: Intelligence Science and Technology Press Inc.

URL: https://doi.org/10.37965/jdmd.2022.67

DOI: 10.37965/jdmd.2022.67

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