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Proceedings Paper

Real-time condition monitoring based on analysis of vibration signal with wavelet transform
Author(s): Hua Liu; Hong Zhang; Aili Qin
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Paper 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.

Paper Details

Date Published: 13 October 2008
PDF: 4 pages
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); doi: 10.1117/12.807383
Show Author Affiliations
Hua Liu, Hebei Univ. of Engineering (China)
Hong Zhang, Hebei Univ. of Engineering (China)
Aili Qin, Hebei Univ. of Engineering (China)


Published in SPIE Proceedings Vol. 7129:
Seventh International Symposium on Instrumentation and Control Technology: Optoelectronic Technology and Instruments, Control Theory and Automation, and Space Exploration

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