无监督学习驱动的高端轴承故障智能诊断算法

Unsupervised learning-driven intelligent fault diagnosis algorithm for high-end bearing

谱峭度是用于滚动轴承故障诊断的有效方法,然而它对工程中常见的偶发性冲击特征具有高度的敏感性,常导致特征提取结果失效.为解决该问题,针对多重偶发性冲击(sporadic impulses,SIs)干扰下的时间序列峭度计算提出了一种基于无监督学习的智能评估方法.首先,根据偶发性冲击在时域上能量高度集中的特性,在时域将原始信号样本等间隔地划分为数据片段.其次,将各数据片段映射到统计参数特征空间(statistical parameter feature space,SPFS).再次,提出了迭代二均值聚类算法,实现了聚类中心自定位及类簇数自适应整定,利用含SIs片段与正常片段在SPFS显著的统计特性差异,逐次识别并清除受SIs干扰的片段.最后对未受SIs干扰的正常片段进行峭度信息融合,得到智能峭度估计.结合该智能峭度估计方法与多尺度分解方法提出了高端轴承故障诊断的新方法.通过仿真算例及轴承故障诊断实际案例验证了该方法能够在多成分耦合、多重偶发性冲击干扰的不利影响下准确提取异常故障特征,并在应用中能够兼顾智能化、高鲁棒性及高计算效率.

Spectral kurtosis is an effective method for rolling bearing fault diagnosis.However,it is highly sensitive to common accidental impact features in engineering,which frequently leads to feature extraction failure.To solve this problem,a robust evaluation method based on unsupervised learning is proposed to calculate the kurtosis of time series under the interference of multiple sporadic impulses(MSIs).First,dynamic signal samples are divided into equal intervals according to the characteristics of high energy concentration in the time domain.Second,each block is mapped to statistical parameter feature space(SPFS).Third,based on the significant statistical difference between MSI and other components in SPFS,an isolated 2-means clustering algorithm is proposed that realizes the self-location of clustering center and self-adaptive adjustment of clustering number and identifies and suppresses blocks receiving interference in the signal step by step.Finally,kurtosis information fusion is performed for the remaining samples,excluding MSI interference.A new method for high-end bearing fault diagnosis is proposed,which is combined with the robust kurtosis evaluation method and the multi-scale decomposition method.Through simulations and actual bearing fault diagnosis,it is verified that the method can accurately extract abnormal fault features under the adverse effects of multicomponent coupling and multiple accidental impact interference and can consider intelligence,high robustness,and high computational efficiency in applications.