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Design of fault detection and isolation via wavelet analysis and neural network | IEEE Conference Publication | IEEE Xplore

Design of fault detection and isolation via wavelet analysis and neural network


Abstract:

A knowledge-based FDI scheme is developed by integrating the time-frequency signal processing technique with neural network design. Wavelet analysis is applied to capture...Show More

Abstract:

A knowledge-based FDI scheme is developed by integrating the time-frequency signal processing technique with neural network design. Wavelet analysis is applied to capture the fault-induced transients in the measured signals and, furthermore, the decomposed signals can be used to extract details about the fault. A Regional Self-Organizing feature Map (R-SOM) neural network is then used to isolate the fault. The R-SOM neural network proposed in this paper has achieved higher clustering and matching-up precision compared with the conventional SOM network, especially when noise, disturbance and other uncertainties occur in the system.
Date of Conference: 30-30 October 2002
Date Added to IEEE Xplore: 06 February 2003
Print ISBN:0-7803-7620-X
Print ISSN: 2158-9860
Conference Location: Vancouver, BC, Canada

References

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