基于O-SVD与FSC的滚动轴承微弱故障特征提取研究

Weak fault feature extraction of bearing based on O-SVD and FSC

由于滚动轴承的早期微弱故障特征难以被完整地提取出来,为此,提出了一种基于周期优选奇异值分解(O-SVD)和快速谱相关(FSC)的滚动轴承微弱故障提取算法。首先,通过理论和仿真分析,对存在细节特征丢失问题的传统截断奇异值分解(T-SVD)算法进行了改进,提出了一种以相关系数作为指标,判断有效奇异值分解子空间的O-SVD算法;然后,将O-SVD作为信号处理的前置处理单元,对滚动轴承的故障信号进行了分解重构,并将处理后的重构信号进行了快速谱相关计算,得到了特征明显且能够较好保存局部细节特征的增强包络谱;最后,基于仿真模型,分析了现有算法的不足,并以故障识别率为指标,阐明了基于O-SVD与FSC的算法在低信噪比工况下的工程适用性。研究结果表明:与对比算法相比,在滚动轴承早期微弱故障、复合故障和综合故障3种工况下,基于O-SVD与FSC的算法均能够较为完整地提取故障信号特征,具有较好的工程适用性。

Aiming at the problem that the early weak faults of rolling bearing was difficult to be extracted completely,an algorithm for the detection of weak faults in bearings based on the fast spectral correlation(FSC)and periodic optimum singular value decomposition(O-SVD)was proposed.Firstly,through theoretical and simulation analysis,the traditional truncated singular value decomposition(T-SVD)algorithm with missing detail features was improved,and the O-SVD algorithm with Correlation Coefficient as index to judge effective singular value decomposition sub-space was proposed.Then,the period optimized SVD was used as the pre-processing unit to decompose and reconstructed the fault signal of rolling bearing,then the reconstructed signal was computed by fast spectral correlation.The enhanced envelope spectra with obvious features and good preservation of local detail features were obtained.Finally,based on the simulation model,the shortcomings of the existing algorithms were analyzed,and the applicability of the proposed algorithm under low signal-to-noise ratio(SNR)was illustrated with fault recognition rate as an index.The results show that,compared with the contrast algorithm,the proposed algorithm can extract the features of fault signals completely under the conditions of early weak fault,compound fault and composite fault,and has good engineering applicability.