基于角域AR模型滤波的滚动轴承故障诊断

在齿轮噪源存在的变转速滚动轴承故障诊断过程中,因混合信号中转频分量相对较小,使得基于时频表达的阶比跟踪技术受到限制。虽然基于故障特征频率的角域重采样能提取轴承的故障特征,但这种算法不能确定故障位置,而且可能会出现误判。针对这一问题,提出了基于角域自回归(auto regressive,简称AR)模型滤波的处理方法。该方法利用线调频小波路径追踪算法从降采样处理的混合信号中提取齿轮瞬时啮合频率趋势线并估计转速,根据估计转速信息对原混合信号进行等角度重采样,获得了角域信号。利用角域信号中齿轮啮合振动成分具有周期性的特点,使用AR模型对其滤波,并且对滤波后信号进行包络阶比分析,完成故障判断。通过处理仿真信号和实验信号,验证了该方法不仅能有效地去除齿轮噪声,并且可以判断轴承故障位置。

Bearing Fault Diagnosis Based on AR Model Filtering in Angle Domain

The algorithm of employing order tracking based on the time-frequency representation is limited in rolling bearing fault diagnosis under a variable rotational speed and gear vibration noise, because of the lack of extractable rotational frequency components. The algorithm of angle domain resampling based on fault characteristic frequency can extract the bearing fault characteristics, however the location of the fault cannot be found and error may occur in this algorithm. The method is proposed in this paper based on auto regressive (AR) model filtering in angle domain to solve this problem.To estimate the rotational speed, the gear instantaneous meshing frequency is extracted from the down sampling mixed signal using chirplet path pursuit algorithm. The mixed signal is re-sampled by a constant angular interval based on the estimated rotational speed. The gear noise is removed in the angle domain signal used AR model. Finally, the fault diagnosis is completed by observing the order spectrum gotten by Hilbert transformation and FFT. The effectiveness of the method is tested by the analysis of the simulation signal and experimental signal.