基于旋转编码器信号的滚动轴承故障特征增强提取

Enhanced extraction of rolling bearing fault features based on rotary encoder signals

针对滚动轴承故障特征较弱且振动传感器安装受限的场合下故障特征不易提取的问题。结合旋转编码器信号传递路径短、干扰少等优势,提出一种基于旋转编码器瞬时角速度(instantaneous angular speed,IAS)信号的滚动轴承故障特征增强提取法。首先,使用去相位算法(de-phasing algorithm,DPA)抑制转频及其谐波等严格周期性分量;其次,通过多点优化最小熵反褶积(multi-point optimization minimum entropy deconvolution adjusted,MOMEDA)增强滚动轴承故障冲击分量;最后,对增强后的信号进行频谱分析,提取轴承故障冲击特征。通过仿真和轴承外圈实测数据验证所提方法的有效性。

For the occasion when the fault characteristics of rolling bearings are weak and the installation of vibration sensors is limited,fault characteristics are not easy to extract.In this paper,combined with the advantages of the short signal transmission path and little interference of the rotary encoder,a method was proposed for enhancing the fault characteristics of rolling bearings based on instantaneous angular speed(IAS)signal of the rotary encoder.Firstly,the de-phasing algorithm(DPA)was used to suppress the strict periodic components such as the frequency conversion and its harmonics;secondly,the bearing fault impact component was enhanced by multi-point optimization minimum entropy deconvolution adjusted(MOMEDA);finally,the enhanced signal was analyzed in the spectrum to extract the bearing fault impact characteristics.The effectiveness of the proposed method was verified by simulation and measured data of a bearing outer ring.