DocumentCode :
2780177
Title :
Time frequency distribution for vibration signal analysis with application to turbo-generator fault diagnosis
Author :
Hua, Liu ; Zhanfeng, Li ; Zhaowei
Author_Institution :
Hebei Univ. of Eng., Handan, China
fYear :
2009
fDate :
17-19 June 2009
Firstpage :
5492
Lastpage :
5495
Abstract :
With the growing demand for improving reliability and performance of turbo-generator set as an important power supply in modern electric industry, the vibration signal processing and fault diagnosis techniques have been developed for dynamic feature extraction and pattern recognition. A new approach using wavelet transform and fuzzy network modeling is presented for vibration fault diagnosis of turbo-generator set. The wavelet transform decomposes the vibration signal in time domain into a two-dimensional function in time-scale plane, which analyzes the low-frequency component of signal with a wide duration function and analyzes conversely high-frequency component with short-duration function. The wavelet fuzzy method can generate dilation and translation parameters, and the network output vector is a linear combination of fuzzy wavelet basis functions. The approximation procedure can achieve better approximation of accuracy order than normal neural network and fuzzy system. The experiment results and analysis approve that the proposed approach is effective, improving the accuracy and reliability of fault diagnosis technology for rotating machinery vibration.
Keywords :
fault diagnosis; feature extraction; fuzzy neural nets; power engineering computing; power generation reliability; time-frequency analysis; turbogenerators; vibrations; wavelet transforms; dynamic feature extraction; electric industry; fuzzy network modeling; fuzzy wavelet basis functions; neural network; pattern recognition; power supply; rotating machinery vibration; time domain; time frequency distribution; time-scale plane; turbo-generator fault diagnosis; two-dimensional function; vibration signal analysis; vibration signal processing; wavelet fuzzy method; wavelet transform; Electricity supply industry; Fault diagnosis; Feature extraction; Fuzzy sets; Pattern recognition; Signal analysis; Signal processing; Time frequency analysis; Wavelet analysis; Wavelet transforms; Reliability; electric industry; fuzzy theory; pattern recognition; turbo-generator set; vibration signal; wavelet transform;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control and Decision Conference, 2009. CCDC '09. Chinese
Conference_Location :
Guilin
Print_ISBN :
978-1-4244-2722-2
Electronic_ISBN :
978-1-4244-2723-9
Type :
conf
DOI :
10.1109/CCDC.2009.5191783
Filename :
5191783
Link To Document :
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