在噪声干扰较强的环境下,为了克服傅里叶分解方法(Fourier Decomposition Method,FDM)在分析调制信号及单独使用调制信号双谱(Modulated Signal Bispectrum,MSB)在分析非平稳信号方面的不足,提出了一种FDM和MSB相结合的滚动轴承故障诊断方法.首先,使用FDM按照高频到低频的方式搜寻傅里叶固有模态函数分量(Fourier Intrinsic band Func-tions,FIBFs);以加权峭度指标作为评判标准,对信号进行重构,确保得到最佳的信号;然后对新的信号利用MSB分析方法进行解调处理,最终通过复合切片谱实现故障特征频率的提取.最后,通过上述方法对模拟信号和滚动轴承外圈故障信号进行分析,其研究结果表明:该方法能够有效地提取故障特征频率,并且与常规双谱进行对比,验证所提方法的优越性.
Fault Diagnosis of Rolling Bearing Based on Fourier Decomposition and Modulated Signal Bispectrum
In an environment with strong noise interference,in order to overcome the shortage of Fourier decomposition method(FDM)in analyzing modulated signals and using Modulated signal bispectrum(MSB)alone to analyze non-stationary signals,a rolling bearing fault diagnosis method is proposed which combine Fourier decomposition method and Modulated signal bispec-trum.First,the FDM was used to search for fourier intrinsic band functions(FIBFs)by high frequency to low frequency,and the weighted kurtosis index was used as the criterion to get the best signal;then the new signal was demodulated by using modulation signal bispectrum and finally the fault characteristic frequency was extracted through the composite slice spectrum.Finally,the analog signal and the fault signal of the rolling bearing outer ring are analyzed by the above method.The results show that the method can extract the fault characteristic effectively and compared with the conventional bispectrum to verify the superiority.
Fourier Decomposition MethodWeighted Kurtosis IndexModulated Signal BispectrumFault Diag-nosisRolling Bearing
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