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Enhanced Process Fault Diagnosis Through Integrating Neural Networks and Andrews Plot

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


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With industrial production processes becoming more and more sophisticated, traditional fault diagnosis systems maynot 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.

Publication metadata

Author(s): Wang S, Zhang J

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: 24th International Conference on Methods and Models in Automation and Robotics (MMAR2019)

Year of Conference: 2019

Pages: 36-41

Online publication date: 14/10/2019

Acceptance date: 20/05/2019

Publisher: IEEE


DOI: 10.1109/MMAR.2019.8864615

Library holdings: Search Newcastle University Library for this item

ISBN: 9781728109343