基于模型辨识的滚动轴承故障诊断

为了解决小样本环境和早期故障预示问题,研究一种基于物理模型辨识的滚动轴承故障诊断方法,即通过物理模型构建标准模式数据库,进而识别故障。考虑到振动传递路径结合界面动态接触机制,建立了轴承表面缺陷的物理模型,通过仿真获得不同损伤位置的振动信号,求得特征矩阵。由于实际测试信号故障特征比较微弱,提出一种盲反卷积和峭度最优Laplace小波相结合的算法,该算法被用于仿真信号与实际工程中微弱冲击信号的检测中,有效突出了冲击成分。最后,以实测信号特征值作为输入,利用距离函数求出与输入值最近的样本点,进而预测出故障位置。案例分析表明,该方法具有较好的可行性与可靠性。

Rolling Element Bearings Fault Diagnosis Based on Physical Model Identification

In order to solve the problem of small-samples and incipient fault prognosis, a novel identification approach based on physical model is presented for automatic diagnosis of defective rolling element bearings. The major advantage of this method is that its training can be performed using simulation data. Prediction of the vibration response due to defect requires an accurate model. Multi-body dynamics of rolling element bearing are developed according to the vibration transmission path combining with dynamics contact mechanism of interface. For the purpose of extracting the feature of weak impact component, a new detecting method based on Blind deconvolution and Kurtosis-Laplace wavelet is proposed. The simulation and the detection of engineering faint impact signal results demonstrate that this method is highly effective in noise reduction and fault feature extraction. Then, through translating the inverse problem into geometric distance matching, the defects can be predicted. Finally, experimental data is used to verify the feasibility and reliability of current method.