变转速工况下基于快速谱平均峭度图的列车轮对轴承故障诊断

Fault Diagnosis of Train Wheelset Bearings Based on Fast Average Kurtogram under Variable Speed Conditions

由于受到轮轨激励的干扰,轮对轴承故障特征易被背景噪声所淹没,且在变转速模式下故障特征具有时变性,进一步加剧了提取难度。为此,提出一种基于快速谱平均峭度图的列车轮对轴承故障特征提取方法。根据频谱趋势将频谱划分为若干子带,采用Meyer小波构造带通滤波器,计算每个窄带滤波信号的平均峭度并构造快速谱平均峭度图;选取最优频带进行解调,消除背景噪声的影响;通过相位函数对滤波信号进行迭代广义解调,解决时变故障冲击间隔带来的频谱模糊问题。仿真和试验分析结果表明所提方法有效克服了强背景噪声和时变故障特征的干扰,为变转速工况下轮对轴承故障特征提取提供了新的解决策略。

Due to the interference of wheel-rail excitation,wheel-set bearing fault characteristics are easily submerged by background noise.The time-varying fault characteristics in variable speed mode further aggravate the difficulty of extraction.For this purpose,a fault feature extraction method for train wheelset bearings was proposed based on the fast spectral average kurtogram.By dividing spectrum into several sub-bands according to the spectral trend,and using the Meyer wavelet to construct a band-pass filter,the average kurtosis of each narrowband filtered signal was calculated and a fast spectral average kurtogram was constructed.Based on the selection of the optimal frequency band for demodulation to eliminate the influence of background noise,the iterative generalized demodulation of the filtered signal was carried out by phase function to solve the spectrum ambiguity caused by time-varying fault impact intervals.The simulation and experimental analysis results show that the proposed method effectively overcomes the interference of strong background noise and time-varying fault features,and provides a new solution strategy for wheel-set bearing fault feature extraction under variable speed conditions.