联合TVF-EMD和SSA降噪的轴承故障特征提取

Fault feature extraction of rolling bearings based on TVF-EMD and SSA noise reduction

针对滚动轴承早期故障信号微弱、故障特征难以提取的问题,文章提出了一种基于时变滤波经验模态分解(time-varying filtering based empirical mode decomposition,TVF-EMD)模态分量自适应融合与奇异谱分析(singular spectrum analysis,SSA)降噪的滚动轴承早期故障特征提取方法。首先,为了降低故障信号的非线性和非平稳性,通过TVF-EMD将轴承信号分解为一系列内蕴模态函数(IMF)。其次,为了克服TVF-EMD分解后IMF分量过多的不足,利用IMF的峭度、复杂度和分形维数构造了复合敏感模态判定因子(composite sensitive mode determination factor,CSMDF),通过CSMDF对IMF分量进行降序排列,并依据复合敏感模态判定因子递增原则对IMF分量依次进行融合,直至找到最优融合分量。最后,通过SSA对最优融合分量降噪,对降噪后分量进行Hilbert包络谱分析,实现轴承故障的特征提取。通过仿真故障信号以及两个实测故障信号对所提方法的性能进行了试验分析,试验结果表明,该方法具有良好的敏感特征筛选融合能力和降噪能力,能更准确地提取出轴承早期故障特征,实现噪声环境下轴承故障类型的准确识别。

Aiming at the difficulty of extracting weak early fault features of rolling bearings,a novel method based on time-varying filtering empirical mode decomposition(TVF-EMD)adaptive fusion of mode components and singular spectrum analysis(SSA)noise reduction was proposed.Firstly,in order to reduce the nonlinearity and non-stationarity of the fault signal,the bearing signal is decomposed into a series of intrinsic mode functions(IMF)by TVF-EMD.Secondly,in order to overcome the deficiency of excessive IMF components after TVF-EMD decomposition,a composite sensitive modal decision factor(CSMDF)was constructed using the curtosis,complexity and fractal dimension of IMF.The CSMDF was used to arrange the IMF components in descending order,and the IMF components were fused successively according to the increasing principle of the composite sensitive modal decision factor.Finally,the optimized fusion component is denoised by SSA,and the denoised component is analyzed by Hilbert envelope spectrum to realize the feature extraction of bearing faults.The performance of the proposed method is analyzed experimentally by simulating fault signals and two measured fault signals.The experimental results show that the proposed method has good ability of sensitive feature screening and fusion and noise reduction,and can extract the early bearing fault characteristics more accurately,and realize the accurate identification of bearing fault types under noisy environment.