Application of Wavelet Packets and GA-BP algorithm in Fault Diagnosis for Diesel Valve Gap Abnormal Fault
Analysing vibration signal is an effective important method for diesel engine fault diagnosis, and its key techniques are feature extraction and pattern recognition. In this paper, wavelet packet decomposition algorithm as an effective method for fault feature extraction is used to decompose the vibration signals, and its percentage of energy band wavelet packet and wavelet packet energy spectrum entropy are regarded as diagnostic feature vectors. At the same time, in the process of pattern recognition, a mixedneural network training algorithm——GA-BP algorithm wasused to recognize the fault pattern in fault diagnosis of valve gap abnormal fault. This method can effectively and reliably be used in the fault diagnosis of valve gap abnormal fault by comparing the two algorithms and analyzing the results of real examples. This method can also effectively be used in other fields.
Wavelet package Spectrum entropy Neural Network BP algorithm Genetic algorithm Fault diagnosis Diesel
Yanping CAI Aihua LI Yanping HE Tao WANG Jinru ZHAO
Xian High-Tech Institute 710025 Xian china
国际会议
The 2nd IEEE International Conference on Advanced Computer Control(第二届先进计算机控制国际会议 ICACC 2010)
沈阳
英文
621-625
2010-03-27（万方平台首次上网日期，不代表论文的发表时间）