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

Lookup NU author(s): Dr Jie ZhangORCiD


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With industrial production processes becoming more and more sophisticated, traditional fault diagnosis systemsmay be insufficient to meet current industrial diagnostic performance requirements. In order to improve fault diagnosisperformance, 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-dimensionalfeatures are compressed by autoencoder to an appropriate dimension, which are then fed to a neural network for faultclassification. Application to a simulated CSTR process demonstrates that the proposed method can give more reliableand earlier diagnosis than the traditional neural network based fault diagnosis method.

Publication metadata

Author(s): Wang S, Zhang J

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: 18th IEEE International Conference on Industrial Informatics (INDIN2020)

Year of Conference: 2020

Pages: 787-792

Print publication date: 20/07/2020

Online publication date: 20/07/2020

Acceptance date: 16/06/2020

Publisher: IEEE

Library holdings: Search Newcastle University Library for this item

ISBN: 9781728149639