【摘要】 为了自动辨识不同尺度下的轴承故障,建立了一种基于多小波包系数样本熵和BP神经网络的模式判别方法。针对5种尺度下的轴承外圈故障信号,分别采用GHM多小波包完成三层分解。为了充分利用多小波包的分析优势,将分解后的16个频段信号分别求系数样本熵,并将其作为神经网络的输入向量。通过三层BP神经网络的训练、学习,并与dB10小波包神经网络做了对比研究。结果表明,多小波包样本熵可以区别不同损伤程度的故障信号,且多小波包样本熵与神经网络结合,其辨识精度更高,分类效果明显优于传统单小波,便于轴承损伤程度的自动识别。更多还原

【Abstract】 In order to automatically recognize different scales of bearing faults,a method of pattern recognition based on the multiwavelet packet sample entropy method and BP neural network is put forth.First,the vibration signals of rolling bearings with five different scaled outer race defects are decomposed into three layers using the GHM multiwavelet packet.The signal sample entropy of 16 decomposed frequency bands are then used as the neural network′s input vector,so that the complete information of the multiwavelet packet decomposition can be thoroughly utilized.Based on the learning and training of a three-layer BP neural network,and in comparison with the dB10 wavelet packet,it can be concluded that the convergence speed and identification accuracy of the multiwavelet packet sample entropy method is much better than those of the traditional wavelet neural network classification.As a result,the multiwavelet packet sample entropy method is effective in automatically recognizing different scales of bearing faults.更多还原