基于自适应滤波的滚动轴承故障诊断研究

针对固有频率未知的滚动轴承故障诊断问题，提出了一种基于经验模态分解的自适应滤波方法。讨论了经验模态分解方法及其在获取固有模态函数过程中的自适应滤波特性。通过对滚动轴承故障振动信号进行经验模态分解得到固有模态函数，运用希尔伯特变换解调固有模态函数得到包络幅频图，获取滚动轴承故障特征频率，进而确定滚动轴承的故障位置。应用该方法对仿真和实际数据进行了分析，并与冲击脉冲法作了比较。结果表明，基于经验模态分解自适应滤波的滚动轴承振动信号解调方法能够更有效地突出故障特征频率成分，避免误诊断。

Rolling bearing fault diagnosis based on adaptive filtering

An adaptive filtering method based on empirical mode decomposition(EMD) was proposed to resolve the fault diagnosis problem of rolling bearing with unknown intrinsic frequency.The EMD method and its adaptive filtering property in the process of obtaining the intrinsic mode function(IMF) were discussed.The rolling bearing vibration signal was refined by EMD method to extract the IMFs,and the IMFs were demodulated by Hilbert transform,then the rolling bearing's characteristic fault frequency was identified by enveloped normalized amplitude-frequency spectrum.The rolling bearing's fault location was confirmed by comparing the characteristic fault frequency resulting from experiment with that from theoretical computation.Simulation and experimental results show that compared with the shock pulse method based on band-pass filter,this method can identify fault frequency more clearly and avoid faulty diagnosis.