最大相关峭度反褶积与傅里叶分解方法相结合的滚动轴承故障诊断

Rolling Bearing Fault Diagnosis of Maximum Correlation Kurtosis Deconvolution Combining with Fourier Decomposition Method

针对强背景噪声环境下滚动轴承故障特征难以提取的问题,提出一种基于最大相关峭度反褶积(MCKD)与傅里叶分解方法(FDM)相结合的滚动轴承故障诊断方法。首先采用MCKD对振动信号去噪、提取与故障相关的冲击成分;其次,采用FDM对去噪信号进行分解,得到若干个瞬时频率具有物理意义的傅里叶固有频带函数和一个残余分量之和;第三,依据各个模态与去噪信号的相关性提取包含故障信息的最优模态分量,并对它们进行重构;最后,计算重构信号的包络谱,从谱图中读取故障信息。将所提故障诊断方法应用于滚动轴承故障仿真和实验数据分析,并通过与现有方法进行对比,结果表明,该方法优于所对比的方法。

Aiming at the problem that it is difficult to extract fault features of rolling bearing form strong background noise,a fault diagnosis method based on maximum correlation kurtosis deconvolution(MCKD)and Fourier decomposition method(FDM)is proposed.First,MCKD is used to denoise the vibration signal and extract the impact component related with failure.Second,the denoising signal is decomposed by FDM,and several Fourier intrinsic band functions(FIBFs)with physical significance and one residual component are obtained.Third,the correlation between each FIBF and denoising signal is computed to select the optimal components that contain main fault information for reconstruction.Finally,the envelope spectrum of the reconstructed signal is calculated and the fault information is read from envelope spectrum for diagnostics.The proposed fault diagnosis method is applied to simulation and experimental data analysis of faulty rolling bearing by comparing with the existing methods.The analysis results show that the proposed method is superior to the existing methods of comparison.