为了能有效地识别滚动轴承故障信号的非平稳和调制特点,提出了一种基于小波分析和Hilbert谱分析的滚动轴承故障诊断的新方法。使用小波分析对包含故障信息的信号进行分解、重构。进一步应用Hilbert变换进行解调和细化频谱分析。结果表明,小波分析和Hilbert变换的联合能够有效地提取故障特征频率并判断故障类型,非常适合滚动轴承的故障诊断。 In order to effectively identify the non-stationary and modulation characteristics of rolling bearing fault signals, a new method of fault diagnosis of rolling bearing based on wavelet analysis and Hilbert spectrum analysis is proposed. Using wavelet analysis, the signal containing fault information is decomposed and reconstructed. Further apply Hilbert transform to demodulate and refine spectrum analysis. The results show that the combination of wavelet analysis and Hilbert transform can effectively extract the fault characteristic frequency and determine the fault type, which is very suitable for the fault diagnosis of the rolling bearing.