Fault Diagnosis with Enhanced Neural Network Modelling

Yu, Ding-Li; Chang, Thoon-Khin

doi:10.1007/11760191_53

Ding-Li Yu²¹ &
Thoon-Khin Chang²¹

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3973))

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International Symposium on Neural Networks

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Abstract

A neural network (NN) based fault detection and isolation (FDI) approach for unknown non-linear system is proposed to detect both actuator and sensor faults. An enhanced parallel (independent) NN model is trained to represent the process and used to generate residual. A mean-weight strategy is developed to overcome the un-modelled noise and disturbance problem. A signal pre-processor is also developed to convert the quantitative residual to qualitative form and applied to a NN fault classifier to isolate different faults. The developed techniques are demonstrated with a multi-variable non-linear tank process.

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Authors and Affiliations

Control Systems Research Group, Liverpool John Moores University, UK
Ding-Li Yu & Thoon-Khin Chang

Authors

Ding-Li Yu
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Thoon-Khin Chang
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Editors and Affiliations

Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
Jun Wang
Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China
Zhang Yi
Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A
Jacek M. Zurada
Laboratory for Computational Biology, Shanghai Center for Systems Biomedicine, 800 Dong Chuan Rd, 200240, Shanghai, China
Bao-Liang Lu
School of Electrical and Electronic Engineering, University of Manchester, UK
Hujun Yin

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Yu, DL., Chang, TK. (2006). Fault Diagnosis with Enhanced Neural Network Modelling. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760191_53

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DOI: https://doi.org/10.1007/11760191_53
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34482-7
Online ISBN: 978-3-540-34483-4
eBook Packages: Computer ScienceComputer Science (R0)Springer Nature Proceedings Computer Science

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