基于提升小波包变换和集成支持矢量机的早期故障智能诊断

为了解决机电设备早期故障难以正确识别的问题,有效地提高分类的准确率,提出一种基于提升小波包变换和集成支持矢量机的早期故障智能诊断新方法。首先,该方法采用提升策略构造基于冲击故障信号特征的双正交小波,借助提升小波包变换提取信号的敏感频带特征,从而通过对敏感频带中的小波包系数进行包络解调分析检测出故障特征频率。其次,通过距离评估技术从原始信号和小波包系数的统计特征中选取最优特征集。最后,将最优特征输入到集成支持矢量机中,实现对不同故障类型的识别。将该方法应用于滚动轴承的早期故障诊断中,测试结果表明,该方法能够有效地提取故障特征,具有比单一支持矢量机更好的分类性能,故障诊断准确率更高。

INTELLIGENT DIAGNOSIS FOR INCIPIENT FAULT BASED ON LIFTING WAVELET PACKAGE TRANSFORM AND SUPPORT VECTOR MACHINES ENSEMBLE

In order to solve the problem of correctly identifying incipient fault for electromechanical equipment and improve classification ability, a novel method of incipient fault intelligent diagnosis based on lifting wavelet package transform (LWPT) and support vector machines (SVMs) ensemble, is proposed. Firstly, a biorthogonal wavelet with impact fault property is constructed via lifting scheme, and the LWPT is carried out to extract sensitive frequency-band features from orginal signals. Then the fault characteristic frequencis can be detected by envelope spectrum analysis of wavelet package coefficients (WPCs) of the most sensitive frequency-band. Secondly, with the distance evaluation technique, the optimal features are obtained from the statistical characteristics of original signals and WPCs. Finally, the optimal features are input into the SVMs ensemble to identify the different fault cases. This method was applied to incipient fault diagnosis of rolling element bearings. Testing results show that the proposed method can effectively extract the fault features, and has better classification performance than the single SVMs, with a high classification success rate.