基于非平稳循环特征极坐标增强的曲轴轴承磨损故障诊断

曲轴轴承早期磨损故障特征微弱且呈现非平稳循环特征,提出一种非平稳循环特征极坐标增强方法。利用连续小波变换对发动机振动信号进行处理,然后根据发动机工作过程与配气相位的关系对于每一工作循环数据进行等角度采样,将信号特征由直角坐标系映射到极坐标系并进行同步增强,并利用模糊C均值聚类对故障特征参数进行分类识别。仿真信号的分析对比显示了该方法能够削弱噪声干扰,突出信号特征。运用此方法对某型发动机曲轴轴承磨损信号进行分析,有效地提取了曲轴轴承磨损特征信息,准确识别了曲轴轴承不同磨损故障。

Crankshaft Bearing Wear Fault Diagnosis Based On the Non-stationary Cycle Feature Enhancement

Crankshaft bearing early wear fault feature is weak and non-stationary time-varying. A polar coordinate enhancement method of the non-stationary cycling feature is proposed. Using the continuous wavelet transform to achieve signal time- frequency representation for the engine vibration signals, and then each work cycle data is re-sampled according to engine working process with gas-phase relationship, signal feature in the time-frequency is mapped to the polar coordinate and is synchronously enhanced. Finally, feature parameters are classified by the fuzzy C-means clustering. Simulated signal is used to testify the effectiveness of this method. The results show that the method can weaken the noise disturbance and strengthen the signal feature. Using this method to diagnose crankshaft bearing early wear fault for a certain type engine, effectively extract the crankshaft bearing fault feature information and accurately identify different crankshaft bearing wear faults.