Signal feature extraction based on wavelet fuzzy network with application to mechanical fault diagnosis

Lin Liu; Huaying Wang

doi:10.1117/12.806634

13 October 2008 Signal feature extraction based on wavelet fuzzy network with application to mechanical fault diagnosis

Lin Liu, Huaying Wang

Proceedings Volume 7128, Seventh International Symposium on Instrumentation and Control Technology: Measurement Theory and Systems and Aeronautical Equipment; 712813 (2008) https://doi.org/10.1117/12.806634
Event: Seventh International Symposium on Instrumentation and Control Technology, 2008, Beijing, China

Abstract

To improve the performance of fault diagnosis technology for vibrant faults of aeroengine, a novel approach combining the wavelet transform with fuzzy theory is proposed. The method with statistic rule is used to determine the threshold of each order of wavelet space and the decomposition level adaptively, increasing the signal-noise-ratio. The effective eigenvectors are acquired by wavelet transform and the fault patterns are classified by fuzzy diagnosis equation based on correlation matrix. The fault diagnosis model of aeroengine is established and the extended Kalman filter algorithm is used to fulfill the network structure. Also the robustness of fault diagnosis equation is discussed. By means of choosing enough samples to train the fault diagnosis equation, the type of fault can be determined on basis of the input information representing the faults. The actual applications show that the proposed method can effectively diagnose vibration fault of aeroengine.

Citation Download Citation

Lin Liu and Huaying Wang "Signal feature extraction based on wavelet fuzzy network with application to mechanical fault diagnosis", Proc. SPIE 7128, Seventh International Symposium on Instrumentation and Control Technology: Measurement Theory and Systems and Aeronautical Equipment, 712813 (13 October 2008); https://doi.org/10.1117/12.806634

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