基于改进的经验模态分解的滚动轴承故障诊断研究

传统的时频分析方法不能有效地处理非平稳信号,经验模态分解(EMD)非常适合处理非平稳信号,但结果可能出现伪内禀模态函数(IMF)和不敏感内禀模态函数。针对EMD的不足,提出能量门限法和敏感IMF选择法相结合来识别真IMF和敏感IMF的方法,对同时满足这两个条件的IMF作频谱变换,频谱图上可以清晰呈现故障特征信息。将改进后的EMD应用到滚动轴承故障诊断实例,证明了此方法的可行性和准确性。

Study of Roller Bearing Fault Diagnosis Based on the Improved Empirical Mode Decomposition

The traditional time-frequency analysis method can not effectively process non-stationary signals. The empirical mode decomposition (EMD) is very suitable for processing the non-stationary signals, but the results may appear pseudo-intrinsic mode functions (IMF) and insensitive intrinsic mode functions. For the lack of EMD, the method combined with energy threshold law and sensitive IMF select wears to identify the true IMF and sensitive IMF is proposed, then the spectrum transformation of IMF satisfying these two conditions and the diagnostic results are given. The fault information can be clearly presented on spectrogram. The improved EMD decomposition is applied to the fault diagnosis of rolling bearing to prove the feasibility and accuracy of this method.