旋转机械长历程偏心故障的数值与图形特征获取方法研究

研究采用采样点的波动稳定性描述偏心故障的程度，即通过对旋转机械长历程偏心故障数据采用信息融合、小波阈值降噪、数据压缩、构造特征方阵、计算特征值方差等方法获取故障的数值特征来描述偏心故障程度；利用小波分解获取低频时域信号和二维轴心轨迹方法探寻偏心故障劣化过程中在图形上的本质变化.应用这三种方法获取转子实验平台模拟的长历程偏心故障的数值与图形特征，结果显示，所获取的数值和图形特征均随着故障的劣化而单向变化，因此，三种方法均能够有效表征偏心故障劣化的过程和程度，具有较高的稳定鲁棒性. 

Extraction Methods for Numerical and Graphical Features of Long Course Eccentric Fault of Rotating Machinery

The eccentric fault is one of the typical faults of rotating machinery and it is important to accurately extract the numerical features and graphical features of eccentric fault in fault study. For this reason, some extraction methods were presented, including the fluctuation stability of sampling points was taken to describe the degree of eccentric fault; the information fusion, wavelet threshold de-noise, data compression, constructing features matrix, calculating the variance and so on were used to deal with the long course of eccentric faults data to describe the degree of eccentric fault; the low-frequency time-domain signal obtained by wavelet decomposition and two-dimensional axis trajectory were employed to explore the essence change in the fault deterioration process. The numerical features and graphical features of long course eccentric fault obtained with the three methods were simulated by a rotor experiment platform. Results show that the numerical features and graphical features change unidirectionally with fault deterioration. Therefore, the three methods can accurately track on the process and degree of eccentric fault, and all of them have a high stability robustness.