改进的傅里叶分解方法及其在滚动轴承故障诊断中的应用

An improved Fourier decomposition method and its application in fault diagnosis of rolling bearings

为了克服傅里叶分解方法在频谱扫描过程中容易获得较多相近边界,导致无效分量过多的问题,提出了一种改进的傅里叶分解方法(improved Fourier decomposition method,IFDM),并将其应用到轴承故障诊断中。首先,IFDM以傅里叶变换为基础,通过建立邻域叠加准则,将同大于或同小于特征平均值的若干相邻原始分量进行合并,得到一组傅里叶固有模态函数(Fourier intrinsic mode functions,FIMF),从而减少无效分量。其次,重构峭度值大于均值的若干FIMF分量,提取敏感故障特征信息。然后,采用自适应多尺度加权形态学滤波(adaptive multi-scale weighted morphological filtering,AMWMF)去除重构分量中的无关分量及背景噪声。最后,对滤波信号进行频谱分析。仿真和实测信号的分析结果验证了所提方法在轴承故障诊断中的有效性,同时,与现有方法的对比结果表明了所提方法的优越性。

In order to overcome the problem that the Fourier decomposition method can easily obtain more similar boundaries during the spectrum scanning process,resulting in too many invalid components,an improved Fourier decomposition method(IFDM)was proposed.And this method was applied to bearing fault diagnosis.First,based on the Fourier transform,several adjacent original components that are simultaneously larger or smaller than the feature mean were combined by establishing a neighborhood superposition criterion in IFDM,and a set of Fourier intrinsic mode functions(FIMF)was obtained by the method,thus reducing invalid components.Secondly,some FIMF components with kurtosis value greater than the mean value were reconstructed to extract sensitive fault feature information.Then,adaptive multi-scale weighted morphological filtering(AMWMF)was used to remove irrelevant components and background noise in the reconstructed component.Finally,the filtered signal was analyzed by spectrum.The effectiveness of the proposed method in bearing fault diagnosis was verified by the results of simulation and measured signals.At the same time,the superiority of the proposed method was verified in the comparison results with existing methods.