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A Grey-box Modelling and Its Application in Model-based Fault Detection

Shi, John Z., Gu, Fengshou, Lennox, Barry and Ball, Andrew (2009) A Grey-box Modelling and Its Application in Model-based Fault Detection. International Journal of COMADEM, 12 (1). pp. 21-30. ISSN 1363-7681

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Abstract

In order to provide an accurate and robust model with model-based fault detection, this paper combines a mathematical model and neural networks to develop a grey-box model. In the grey-box model, the mathematical model represents the dominant behaviour of the system, leaving the mismatch part of the system to be approximated by neural networks. The output of the grey-box model is used for residual generation in the model-based fault detection approach. Because the neural network compensates the model error from the mathematical model, a high accuracy model can be obtained and the residual generated under normal conditions can also be minimised by the combination. On the other hand, because most of the mathematical model mismatches exist in transients, the working load of the neural network can be reduced and the network structure can be simplified by the combination. Moreover, the grey box model provides more robust residual than black-box model and it enables the residual signatures to be physically interpretable. The capability of this grey-box model-based approach is evaluated in model accuracy and sensitivity in detecting faults introduced on an electro-hydraulic control system.

Item Type: Article
Additional Information: Publisher permission sought - 15 May 2009 - GS
Uncontrolled Keywords: Neural networks, grey-box model, model-based approach, fault detection, electro-hydraulic system
Subjects: T Technology > TJ Mechanical engineering and machinery
Schools: School of Computing and Engineering
School of Computing and Engineering > Automotive Engineering Research Group
School of Computing and Engineering > Diagnostic Engineering Research Centre
School of Computing and Engineering > Diagnostic Engineering Research Centre > Energy, Emissions and the Environment Research Group
School of Computing and Engineering > Diagnostic Engineering Research Centre > Machinery Condition and Performance Monitoring Research Group
School of Computing and Engineering > Diagnostic Engineering Research Centre > Measurement System and Signal Processing Research Group
School of Computing and Engineering > High-Performance Intelligent Computing
School of Computing and Engineering > High-Performance Intelligent Computing > Information and Systems Engineering Group
Related URLs:
Depositing User: Zhanqun Shi
Date Deposited: 15 May 2009 13:07
Last Modified: 20 Aug 2015 07:46
URI: http://eprints.hud.ac.uk/id/eprint/4336

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