NDLI: Fault Diagnosis Method to Internal-combustion Engine Based on Integration of Scale-wavelet Power Spectrum, Rough Set and Neural Network

Content Provider	IEEE Xplore Digital Library
Author	Chen Baojia Li Li Zhao Xinze
Copyright Year	2007
Description	Author affiliation: China Three Gorges Univ., Yichang (Chen Baojia; Li Li; Zhao Xinze)
Abstract	In order to diagnose the faults of the valve and the piston-connecting rod of internal-combustion engine (ICE), the vibration signals under normal and abnormal models were measured by experiments. Through continuous wavelet transform (CWT), the scale-wavelet power spectrum (SWPS) of signals was obtained. The wavelet power (WP) distribution on different scales of each model is observed to be similar and mainly concentrated in particular scope of 1~32. By analyzing the diversity of SWPS distribution, the WP that is most sensitive to the characteristic of each model were extracted by rough set (RS) theory as feature and taken as input to train the back-propagation neural network (BPNN). By the trained BPNN to diagnose the fault signals under detection, the correctness rate is 100%. The fault diagnosis method based on the integration of the SPWS, RS and neural network demonstrates to be efficient and feasible. It has preferable engineering applicability and referenced value to diagnosis for complex machines.
Starting Page	431
Ending Page	435
File Size	650055
Page Count	5
File Format	PDF
ISBN	9787811240559
DOI	10.1109/CHICC.2006.4346846
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-07-26
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Wavelet transforms Internal-combustion engine Vibration measurement Valves Ice Neural network Engines Rough set Fault diagnosis Continuous wavelet transforms Scale-wavelet power spectrum Fault detection Neural networks Signal detection
Content Type	Text
Resource Type	Article

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