基于小波包分析与多核学习的滚动轴承故障诊断

为了更准确地诊断滚动轴承故障,提出了一种基于小波包分析与多核学习的滚动轴承故障诊断方法.该方法首先对振动信号进行3层小波包分解,将振动信号分解为不同频带的信号,提取各频带的相对能量特征,构建特征向量;然后采用多核学习算法从训练样本集中学习核函数与分类器;最后使用训练出的分类器识别滚动轴承故障类型.为了验证方法的有效性,进行了滚动轴承故障诊断实验,实验结果表明该方法的故障诊断准确率达到98.25%,与传统的基于小波包与支持向量机的滚动轴承故障诊断方法相比,其故障诊断准确率更高,同时由于避免了核函数的选择问题,该方法更便于实际应用.

Rolling bearing fault diagnosis based on wavelet packet analysis and multi kernel learning

To diagnose the rolling bearing fault more accurately, a method based on wavelet packet analysis and multi kernel learning was proposed for rolling bearing fault diagnosis. Firstly, the three-level wavelet packet decomposition algorithm was used to decompose the vibration signals into different frequency bands. The relative energies of each frequency band were computed to form the feature vector. Secondly, multi kernel learning was employed to learn a kernel function and classifier from the training samples. Finally, the trained classifier was used to identify the fault type of rolling bearing. To validate the proposed method, an experiment of fault diagnosis for the rolling bearings was carried out. The results show that the fault diagnosis accuracy rate of the proposed method reaches 98.25%, higher than the traditional rolling bearing fault diagnosis method based on the wavelet packet and support vector machine. Since the problem of kernel function selection is avoided, the proposed method is more convenient for practical application.