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
提出了基于基因优化最小二乘支持向量机(Gene optimized least squares support vector ma⁃chine,GOLSSVM)的自适应局部迭代滤波(Adaptive local iterative fittering,ALIF)和排列熵(Permuta⁃tion entropy,PE)的故障诊断方法,并将该方...提出了基于基因优化最小二乘支持向量机(Gene optimized least squares support vector ma⁃chine,GOLSSVM)的自适应局部迭代滤波(Adaptive local iterative fittering,ALIF)和排列熵(Permuta⁃tion entropy,PE)的故障诊断方法,并将该方法应用于齿轮箱的诊断,成功实现了对齿轮箱4种故障种类的识别。针对排列熵无法直接识别齿轮箱不同故障类别的问题,利用ALIF方法相较于EMD方法在去除残余噪声及抑制模式混叠上的优势,使用ALIF方法对故障信号进行降噪,提取有效分量,再计算有分量的PE值(C-PE值),以获得振动信号的多尺度特性;然后,使用基因算法对最小二乘支持向量机(Least squares support vector machine,LSSVM)进行了优化;最后,将特征向量输入到GOLSSVM,对故障特征进行分类。结果表明,所提方法相比BP神经网络和SVM在故障识别精度上有优势。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
文摘提出了基于基因优化最小二乘支持向量机(Gene optimized least squares support vector ma⁃chine,GOLSSVM)的自适应局部迭代滤波(Adaptive local iterative fittering,ALIF)和排列熵(Permuta⁃tion entropy,PE)的故障诊断方法,并将该方法应用于齿轮箱的诊断,成功实现了对齿轮箱4种故障种类的识别。针对排列熵无法直接识别齿轮箱不同故障类别的问题,利用ALIF方法相较于EMD方法在去除残余噪声及抑制模式混叠上的优势,使用ALIF方法对故障信号进行降噪,提取有效分量,再计算有分量的PE值(C-PE值),以获得振动信号的多尺度特性;然后,使用基因算法对最小二乘支持向量机(Least squares support vector machine,LSSVM)进行了优化;最后,将特征向量输入到GOLSSVM,对故障特征进行分类。结果表明,所提方法相比BP神经网络和SVM在故障识别精度上有优势。
