Real-time condition monitoring based on analysis of vibration signal with wavelet transform

Hua Liu; Hong Zhang; Aili Qin

doi:10.1117/12.807383

13 October 2008 Real-time condition monitoring based on analysis of vibration signal with wavelet transform

Hua Liu, Hong Zhang, Aili Qin

Proceedings Volume 7129, Seventh International Symposium on Instrumentation and Control Technology: Optoelectronic Technology and Instruments, Control Theory and Automation, and Space Exploration; 712913 (2008) https://doi.org/10.1117/12.807383
Event: Seventh International Symposium on Instrumentation and Control Technology, 2008, Beijing, China

Abstract

By combining wavelet analysis and neural network, a new approach for condition monitoring is presented for rotating machine fault. The wavelet analysis can accurately localize the features of transient signal in time-frequency domains. The wavelet transform technology is appropriate for processing of fault signals consisting of short-lived, high-frequency components closely located in time as well as long duration components closely spaced in frequency. In a view of the inter relationship of wavelet decomposition theory, the crucial components as features are inputted into radial basis function for fault pattern recognition. In order to acquire the network parameters, the improved Levenberg-Marquardt optimization technique is used for training process. By choosing enough samples to train wavelet network, the fault pattern can be determined according to the output results. Also, the robustness of wavelet network for fault diagnosis is discussed. The applied results show that the proposed method can improve the performance for real-time monitoring of vibration fault.

Citation Download Citation

Hua Liu, Hong Zhang, and Aili Qin "Real-time condition monitoring based on analysis of vibration signal with wavelet transform", Proc. SPIE 7129, Seventh International Symposium on Instrumentation and Control Technology: Optoelectronic Technology and Instruments, Control Theory and Automation, and Space Exploration, 712913 (13 October 2008); https://doi.org/10.1117/12.807383

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KEYWORDS

Wavelets

Wavelet transforms

Neural networks

Discrete wavelet transforms

Fractal analysis

Signal analyzers

Continuous wavelet transforms

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