Abstract: This article proposes a new method for fault diagnosis in order to accurately determine the fault of rolling bearing. The vibration signal of rolling bearing is decomposed by using the improved wavelet threshold method, that the vibration signal is decomposed and reconstructed by using wavelet packet, the signal feature in every frequency band is extracted as the samples of fault diagnosis to judge bearing fault location according to the energy distribution within each frequency band features. The typical faults of SKF 6205-2RS bearing is simulated and verified in the MATLAB environment, the results prove that the de-noising effect of improved threshold method is better than the traditional method and wavelet packet can accurately extract signal fault characteristics, which can improve the accuracy and effectiveness of bearing fault detection.

Key words: wavelet threshold, wavelet packet, energy vectors, rolling bearings, fault diagnosis

摘要： 为了准确有效地确定滚动轴承的故障部位，提出一种轴承故障诊断的新方法。用改进的小波阈值法对轴承振动信号进行降噪处理，对去噪后的信号进行小波包分解与重构，提取各重构子带内的信号特征作为故障诊断的样本，依据各子带信号的能量分布特征判断轴承的故障部位。在MATLAB环境下对SKF6205-2RS轴承的典型故障进行了仿真研究，结果表明改进的阈值法相比于传统去噪方法有较好的去噪效果，小波包能够准确提取信号的故障特征，能够提高轴承故障检测的准确性和有效性。

关键词: 小波阈值, 小波包, 能量向量, 滚动轴承, 故障诊断