基于小波频谱分析的滚动轴承故障诊断研究

Rolling Bearing Fault Diagnosis Research Based on Wavelet Spectrum Analysis

提出了一种基于小波频谱分析的滚动轴承故障诊断方法。利用小波默认阈值方法进行数据消噪处理,并对消噪后振动数据进行了5层小波分解。根据轴承故障特征频率,对故障特征频率所在层进行小波重构,计算功率谱密度。对滚动轴承故障的振动信号的仿真结果表明,该方法能有效识别滚动轴承的内圈、外圈和滚动体故障。

The rolling bearing fault diagnosis method is proposed. Wavelet default threshold method is adopted to make data denoising processing, and 5 vibration data. Wavelet reconstruction is done for layer wavelet decomposition is made to denoised the layer of fault characteristic frequency, power spectral density is computed finally. The simulation result for vibration signal of rolling bearing fault denotes that this method can effectively identify the inner ring, outer ring and rolling body faults of rolling bearing.