基于MED-权重包络谱的轮对轴承故障特征增强

Cyclic Shock Enhancement based on MED and Weight Envelope Spectrum for Wheel Bearings

NLM权重包络谱方法的诊断效果取决于信号中故障冲击点和噪声点之间的相异性,其在处理低信噪比信号时效果并不理想。针对该问题,提出将最小熵解卷积方法引入到权重包络谱方法的预处理中。首先对信号进行最小熵解卷积(Minimum-entropy deconvolution,MED)处理以初步消除信号中的传递噪声干扰,增大信号中故障冲击点和噪声点的相异性;而后对滤波后的信号进行加权运算,获得权重包络曲线,从权重的角度分离故障点与噪声点,使故障冲击特征得到二次增强;最后通过分析权重包络曲线包络谱得到诊断结果。应用仿真、实验数据和工程数据分析验证了该方法的有效性。分析结果表明,所提方法能够改善NLM权重包络谱方法的应用效果,在消除背景噪声、挖掘故障信息、保证故障诊断准确性方面有较大优势。

The diagnosis effect of weighted envelope spectrum algorithm based on non-local means algorithm(NLM)depends on the difference between fault shock points and noise points.The effect is unsatisfactory in the case of low SNR.Aiming at such a dilemma,the minimum entropy deconvolution(MED)is introduced into the preprocessing of the weighted envelope spectrum algorithm to remedy this defect.Firstly,the MED is exploited to preprocess the signals to eliminate the in-terference of signals transfer path and enhance the difference between the fault shock points and the noise points,and the fault shock points and the noise points are separated from the weight points.Then,the weighted envelope curve is obtained by weighting the filtered signal,and the cyclic impact feature is further enhanced in the form of weight by the weighted oper-ation.Finally,the health condition of bearings can be determined by means of frequency spectrum of the weighted envelope.The effectiveness of the proposed method is verified by simulation,laboratory test and engineering data analysis.The results show that the proposed method can improve the application effect of the weighted envelope spectrum,and has a great superi-ority in eliminating background noise,extracting fault information and ensuring the accuracy of fault diagnosis.