基于小波包能量熵和GWO-SVM的滚动轴承故障诊断

针对滚动轴承不同故障类型和不同损伤程度识别准确率较低的问题,提出了将小波包能量熵、灰狼优化算法和支持向量机相结合的故障诊断方法.首先,将滚动轴承振动信号进行3层小波包分解,对第3层各频段小波包分解系数进行重构,提取各频段成分的能量熵构成故障特征向量;其次,利用灰狼优化算法实现支持向量机参数优化;最后,基于优化后的支持向量机分类模型完成对测试集滚动轴承不同故障类型和不同损伤程度特征向量的识别诊断.实验结果表明,相比实验和文献中其他方法,该方法对滚动轴承不同故障类型和不同损伤程度具有更加突出的故障辨识能力.

Rolling bearing fault diagnosis based on wavelet packet energy entropy and GWO-SVM

Aiming at the problem of low recognition accuracy of rolling bearings with different fault types and damage degrees, a fault diagnosis method combining wavelet packet energy entropy, gref wolf optimizer (GWO) and support vector machine (SVM) was proposed. Firstly, the rolling bearing vibration signal was decomposed by three-layer wavelet packet, following reconstructing the wavelet packet decomposition coefficients of each frequency band in the third layer, and the energy entropy of components in each frequency band was extracted to form fault feature vectors. Secondly, GWO was used to optimize SVM parameters. Finally, based on the optimized SVM classification model, the feature vectors of different fault types and damage degrees of rolling bearings in the test set were recognized and diagnosed. The experimental results show that the proposed method has more outstanding fault identification ability for both different fault types and different damage degrees of rolling bearings than other methods in experiments and literatures.