一种多层经验小波变换与多指标交叉融合的列车轮对轴承故障诊断研究

Fault diagnosis of train wheelset bearing based on multi-later EWT and multi-index cross fusion

列车轮对轴承在长期使用过程中极易产生各类故障,但恶劣的工况导致其故障诊断较为困难,针对这一问题,提出了一种基于多层经验小波变换(multi-layer empirical wavelet transform,MLEWT)与多指标交叉融合的列车轮对轴承故障诊断方法。所提MLEWT方法在划分信号频谱边界过程中,不再以局部极值点作为频谱区间划分依据,而是通过设定频谱区间个数来对整个信号频谱进行多层分解,得到多个模态分量信号。提出了一种基于交叉融合峭度、平滑因子、稀疏值和峰值系数4个统计量指标的故障稀疏度大小评价方法,该方法将多个统计量指标综合考虑,有效克服了单一指标存在的不足,自适应搜寻信号MLEWT后最优的模态分量信号。通过对最优模态分量信号进行包络解调分析诊断出轴承故障。仿真信号和实际轮对轴承故障信号的分析结果表明:所提方法可以有效提取轴承故障特征信息,诊断效果优于传统的谱峭度和EWT方法。

Train wheelset bearings are extremely prone to various types of damages during long-term service.However,fault detection of wheelset bearing is difficult due to the harsh working conditions.To address this issue,a hybrid multi-layer empirical wavelet transform and multi-index cross fusion for train wheelset bearing fault diagnosis is proposed in this paper.Firstly,the proposed MLEWT method does not use local extreme points to divide the spectrum interval in the segment of signal Fourier spectrum.Instead,the number of spectrum intervals is set to perform a more detailed multi-layer decomposition of the whole signal spectrum,so as to obtain a number of modal components.Secondly,a multi-index cross fusion method is put forward to adaptively search for the optimal modal component after MLEWT.It can effectively fuse four statistical evaluation indexes,which effectively overcomes the shortcomings of a single index.Finally,the bearing faults are diagnosed by envelope demodulation analysis of the optimal modal component.The proposed method is applied to analyze simulated fault signal and wheelset bearing fault signal,the results show that the proposed method can extract bearing fault features effectively,and the diagnosis effect is better than the fast Kurtogram and traditional EWT methods.