计算机科学 ›› 2020, Vol. 47 ›› Issue (11A): 57-63.doi: 10.11896/jsjkx.190900174
林毅1, 吉鸿江2, 韩佳佳3, 张德平3
LIN Yi1, JI Hong-jiang2, HAN Jia-jia3, ZHANG De-ping3
摘要： 针对以往系统故障诊断方法中存在的多指标相关问题以及考虑多重积分时计算复杂、效率低等问题,文中基于马氏距离(Mahalanobis Distance,MD)度量提出一种系统故障诊断方法,利用采集到的系统状态监控数据,计算观测样本与已知样本之间的马氏距离,根据距离大小的MD面积度量比较判断观测样本类别,对已知数据样本的马氏距离的分布与观测数据样本的马氏距离的分布的差异进行故障诊断。具体地,首先利用MD方法将多变量数据转换为单变量数据,排除多变量之间相关性的干扰,避免了利用多重积分求解多变量联合分布的复杂性以及不确定性;然后利用面积度量法比较单变量数据的累积分布函数之间的差异,根据定积分计算分布曲线之间的面积值,以面积值较小对应的样本故障类别作为观测数据的类别。通过将所提方法与常用故障诊断方法(BP神经网络、朴素贝叶斯)进行比较,证明了其简单有效,故障诊断正确率高,能够大大降低计算成本,并有效地提高故障诊断的效率。
中图分类号:
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