一种强噪声背景下地铁车轮轴承故障信号的特征提取方法

针对地铁车轮轴承的声学法故障诊断中背景噪音大、难以提取出有效故障特征的问题,提出了一种在强噪声背景下故障特征提取的方法。对声音信号进行短时傅里叶变换(short-time Fourier transform,STFT)得到时频图,时频图中的条纹就是故障特征;沿条纹方向将图像各个点的信号强度相加,得到时频图对应的信号强度叠加折线图来展示故障特征,并且提出一种基于峰值高度的自适应循环降噪算法对信号强度叠加折线图进行降噪,得到该折线图的评价指标为有效峰值数目;最后提出一种自适应滑动窗口检测法来截取时频图中条纹分布的区域,以此来得到最优的故障特征展现效果。实验结果表明,所提出的方法可以从采集的音频信号中提取出来明显有效的故障特征。

Feature Extraction Method of Subway Bearing Fault Signal under Strong Noise Background

Aiming at the problem of large background noise and difficulty in extracting effective fault features in the acoustic fault diagnosis of subway wheel bearings, a method for fault feature extraction under strong noise background was proposed. Perform short-time Fourier transform (STFT) on the sound signal to obtain a time-frequency diagram, and the stripes in the time-frequency diagram are the fault features; add the signal intensities of each point of the image along the direction of the stripes to obtain the time-frequency diagram The signal intensity corresponding to the graph is superimposed on a line graph to show the fault characteristics; and an adaptive cyclic noise reduction algorithm based on the peak height is proposed to reduce the noise of the signal intensity superposition line graph, and the evaluation index of the graph is the number of effective peaks; finally An adaptive sliding window detection method is proposed to intercept the fringe distribution area in the time-frequency graph, so as to obtain the optimal fault feature display effect. Experimental results show that the proposed method can extract obvious fault features from the collected audio signals.