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Proceedings Paper

Multi-concurrent fault diagnosis method for turbo-generator set based on wavelet fuzzy network
Author(s): Hua Liu; Yuguo Wang; Baoshe Liang; Songhua Shen
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Paper Abstract

To improve the limitation of applying traditional fault diagnosis method to the diagnosis of multi-concurrent vibrant faults of turbo-generator sets, a new diagnosis approach combining the wavelet transform with fuzzy theory is proposed. A novel method based on the statistic rule is brought forward to determine the threshold of each order of wavelet space and the decomposition level adaptively, increasing the signal-noise-ratio (SNR). The effective eigenvectors are acquired by binary discrete wavelet transform and the fault modes are classified by fuzzy diagnosis equation based on correlation matrix. The fault diagnosis model of turbo-generator set is established and the improved least squares algorithm (LSA) is used to fulfill the network structure and the robustness of fault diagnosis equation is discussed. By means of choosing enough samples to train the fault diagnosis equation and the information representing the faults is input into the trained diagnosis equation, and according to the output result the type of fault an be determined. Actual applications show that the proposed method can effectively diagnose multi-concurrent fault for stator temperature fluctuation and rotor vibration and the diagnosis result is correct.

Paper Details

Date Published: 6 November 2006
PDF: 6 pages
Proc. SPIE 6357, Sixth International Symposium on Instrumentation and Control Technology: Signal Analysis, Measurement Theory, Photo-Electronic Technology, and Artificial Intelligence, 63574S (6 November 2006); doi: 10.1117/12.717472
Show Author Affiliations
Hua Liu, Hebei Univ. of Engineering (China)
Yuguo Wang, Hebei Univ. of Engineering (China)
Baoshe Liang, Hebei Univ. of Engineering (China)
Songhua Shen, Beijing Univ. of Aeronautics and Astronautics (China)


Published in SPIE Proceedings Vol. 6357:
Sixth International Symposium on Instrumentation and Control Technology: Signal Analysis, Measurement Theory, Photo-Electronic Technology, and Artificial Intelligence

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