Bearing fault signal is nonlinear and non-stationary, therefore proposed a fault feature extraction method based on wavelet packet decomposition （WPD） and local mean decomposition （LMD） permutation entropy, which ...Bearing fault signal is nonlinear and non-stationary, therefore proposed a fault feature extraction method based on wavelet packet decomposition （WPD） and local mean decomposition （LMD） permutation entropy, which is based on the support vector machine （SVM） as the feature vector pattern recognition device Firstly, the wavelet packet analysis method is used to denoise the original vibration signal, and the frequency band division and signal reconstruction are carried out according to the characteristic frequency. Then the decomposition of the reconstructed signal is decomposed into a number of product functions （PE） by the local mean decomposition （LMD） , and the permutation entropy of the PF component which contains the main fault information is calculated to realize the feature quantization of the PF component. Finally, the entropy feature vector input multi-classification SVM, which is used to determine the type of fault and fault degree of bearing The experimental results show that the recognition rate of rolling bearing fault diagnosis is 95%. Comparing with other methods, the present this method can effectively extract the features of bearing fault and has a higher recognition accuracy展开更多
汽车变速器早期故障监测与诊断对汽车安全行驶和降低交通事故起着非常重要的作用。针对汽车无级变速器故障特征,提出一种基于互信息熵(Mutual Information Entropy,MIE)理论的故障信号特征提取方法,以及易于实现的多类分类支持向量机(Su...汽车变速器早期故障监测与诊断对汽车安全行驶和降低交通事故起着非常重要的作用。针对汽车无级变速器故障特征,提出一种基于互信息熵(Mutual Information Entropy,MIE)理论的故障信号特征提取方法,以及易于实现的多类分类支持向量机(Support Vector Machine,SVM)算法处理故障状态分类问题。结果表明,将MIE和SVM算法相结合用于汽车无级变速器故障诊断方面是可行的和有效的,并能提高故障监测的可靠性。展开更多
基金supported by the National Natural Science Foundation of China(51375405)Independent Project of the State Key Laboratory of Traction Power(2016TP-10)
文摘Bearing fault signal is nonlinear and non-stationary, therefore proposed a fault feature extraction method based on wavelet packet decomposition （WPD） and local mean decomposition （LMD） permutation entropy, which is based on the support vector machine （SVM） as the feature vector pattern recognition device Firstly, the wavelet packet analysis method is used to denoise the original vibration signal, and the frequency band division and signal reconstruction are carried out according to the characteristic frequency. Then the decomposition of the reconstructed signal is decomposed into a number of product functions （PE） by the local mean decomposition （LMD） , and the permutation entropy of the PF component which contains the main fault information is calculated to realize the feature quantization of the PF component. Finally, the entropy feature vector input multi-classification SVM, which is used to determine the type of fault and fault degree of bearing The experimental results show that the recognition rate of rolling bearing fault diagnosis is 95%. Comparing with other methods, the present this method can effectively extract the features of bearing fault and has a higher recognition accuracy
文摘汽车变速器早期故障监测与诊断对汽车安全行驶和降低交通事故起着非常重要的作用。针对汽车无级变速器故障特征,提出一种基于互信息熵(Mutual Information Entropy,MIE)理论的故障信号特征提取方法,以及易于实现的多类分类支持向量机(Support Vector Machine,SVM)算法处理故障状态分类问题。结果表明,将MIE和SVM算法相结合用于汽车无级变速器故障诊断方面是可行的和有效的,并能提高故障监测的可靠性。
