Application of genetic programming and soft morphological filters to motor rolling bearing fault diagnosis
柔性形态滤波和遗传规划在电机轴承故障诊断的应用

Based on soft morphological filtering and genetic programming(GP), a motor rolling bearing fault diagnosis method is proposed. It is very difficult to filtrate the fault vibration signals from the strong noise background because the roller bearing fault diagnosis is a problem of multi-class classification of inner ring fault, outer ring fault and ball fault. Firstly, vibration signals are filtrated by soft morphological filters. Secondly, the normalized energy in different characteristic frequencies is utilized to identify the fault features of feature terminals of GP. An optimal motor rollingbearing fault classification model is obtained by reproducing, mutating and over-crossing. Experiment results demonstrate that this modeling is correct and precise.