NDLI: Time frequency distribution for vibration signal analysis with application to turbo-generator fault diagnosis

Content Provider	IEEE Xplore Digital Library
Author	Liu Hua Li Zhanfeng Zhaowei
Copyright Year	2009
Description	Author affiliation: Hebei University of Engineering, Handan 056038, China (Liu Hua; Li Zhanfeng; Zhaowei)
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.
Starting Page	5492
Ending Page	5495
File Size	318687
Page Count	4
File Format	PDF
ISBN	9781424427222
DOI	10.1109/CCDC.2009.5191783
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-06-17
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Wavelet transforms fuzzy theory turbo-generator set vibration signal wavelet transform Time frequency analysis Wavelet analysis Pattern recognition Fault diagnosis Fuzzy sets electric industry Signal processing Electricity supply industry Feature extraction Reliability Signal analysis pattern recognition
Content Type	Text
Resource Type	Article

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