MMAS与粗糙集在轴承复合故障诊断中的应用

在分析振动加速度信号的基础上,提出了新的粗糙集属性约简算法,并应用于轴承复合故障诊断.将最大一最小蚂蚁系统(max-min ant system,简称MMAS)引入条件属性约简中,以最坏Fisher准则函数作为启发式信息以提高搜索效率,综合考虑分类正确率和条件属性个数两方面因素,利用粗糙集理论约简故障诊断决策表,有效地提高了轴承故障诊断的效率.

Application of Max-Min Ant System and Rough Sets to Compound Fault Diagnosis of Bearing

An attribute reduction algorithm of rough set is put forward on the ba sis of the analysis of bearing vibration signals. The worst Fisher criterion was adopted as heuristic information to improve the searching efficiency and the max min ant system was selected for the condition attribute reduction in the comp o und fault of a bearing in a gearbox. Considering the classification accuracy and the number of the condition attribute, the fault diagnosis decision table was s implified by using the rough sets theory. The results show that the method can m arkedly raise the efficiency of bearing fault diagnosis.