转子振动故障的小波能谱熵SVM诊断方法

Rotor vibration fault diagnosis method based on wavelet energy spectrum entropy and SVM

融合小波能谱熵和支持向量机(SVM)的特点,提出了基于小波能谱熵的SVM故障诊断方法.利用转子试验台对转子典型振动故障进行模拟并采集振动数据,提取其振动信号的小波能谱熵作为特征向量,通过样本训练建立了转子在各种典型振动故障状态下的SVM模型和多类分类器,进而实现了对未知转子振动故障的识别.实际应用表明,提出的转子振动故障诊断方法是可行和有效性的.

A method for rotor vibration fault diagnosis was proposed based on wavelet energy spectrum entropy and support vector machine （SVM） by fusing their advantages. The typical rotor vibration faults were simulated on the rotor test-bed and vibration signals were collected at the same time. The SVM model and multi-classifier for rotor typical faults diagnosis were established through training of the samples obtained, and the unknown rotor faults were recognized consequently. It is proved by practical applications that the method proposed in this paper for diagnosing rotor vibration faults is workable and effective.