基于CSLBP的轴承信号时频特征提取方法

针对滚动球轴承振动加速度信号特征提取问题,提出一种基于中心对称局部二值模式(center-symmetric local binary pattern,简称CSLBP)的时频特征提取方法。首先,利用广义S变换对滚动球轴承振动加速度信号进行处理,通过采用时频聚集性度量准则自适应地确定广义S变换的调整参数,从而获取时频分辨性较好的二维时频图;然后,计算二维时频图的CSLBP,提取CSLBP纹理谱描述滚动球轴承振动加速度信号的时频特征。对滚动球轴承正常、外圈故障、内圈故障和滚动体故障4种不同状态的振动加速度信号进行了研究。结果表明,CSLBP纹理谱能有效地表达滚动球轴承振动加速度信号的时频特征,与局部二值模式(local binary pattern,简称LBP)和统一模式LBP纹理谱相比,CSLBP纹理谱具有特征维数低和区分性能好的优点。

Time-Frequency Feature Extraction Method Based on CSLBP for Bearing Signals

A time-frequency feature extraction method based on center-symmetric local binary pattern (CSLBP) is proposed for vibration acceleration signals of rolling bearings. First, the generalized S transform is employed to process vibration acceleration signals of rolling bearings. Two-dimensional time-frequency images with good resolution performance of the bearing signals are obtained by utilizing the time-frequency aggregation measurement criterion to adaptively set the adjustment parameter of the generalized S transform. Then, the CSLBP of the images is calculated. Texture spectra of CSLBP are extracted and utilized to describe time-frequency characteristics of the vibration acceleration signals of rolling bearings. Vibration acceleration signals from four different rolling bearing states are studied. The experimental results indicate that the texture spectrum of CSLBP can effectively express the time-frequency characteristics of the vibration acceleration signals of rolling bearings. It has low dimensional feature and satisfactory separability compared with the texture spectra of local binary pattern (LBP) and uniform pattern LBP.