基于数据关联的故障快速检测

 多数情况下，快速实时地进行故障检测是很重要的。将故障看做是通过多传感器观测的动态模型，进行多传感器多模型概率数据关联,以各个模型的关联结果和设定的阈值为依据,可以有效地实现故障检测。联合概率数据关联（JPDA）算法是解决多传感器多目标跟踪的一个有效方法，文中通过分析概率数据关联算法，对联合概率数据关联算法进行了改进：（1）通过正确地选择阈值，移除小概率事件，进而建立一个近似的确认矩阵；（2）根据被跟踪目标故障跟踪门的相交情况，将跟踪空间进行数学划分，形成若干相互独立的区域；（3）对同一区域内公共有效量测的概率密度值进行衰减，计算出关联概率。仿真对比表明，本文的改进算法能显著减少计算时间，有效提高故障检测的快速性和实时性。

Fast Fault Detection Based Data Association

Under most circumstances,it is most important to achieve fast and real-time fault detection.Regarding faults as dynamic modes which are observed through the multi-sensors,with probabilistic data association based on multi-sensor,the fault detection results are obtained according to the association probability and threshold values.Joint probabilistic data association(JPDA) algorithm is one of the effective ways for multi-sensor multi-target tracking,and the JPDA algorithm is here improved: first,an approximation method is proposed for constructing the confirmation matrix through removing the small probability events using the right threshold values;and then,the mathematical division of the confirmation matrix is presented according to the intersection area of the association gate of fault targets to be tracked;lastly,the association probability of fault target is computed through attenuating the value of the public measurement.The simulation results show preliminarily that the improved JPDA algorithm saves the computing time greatly,and effectively meets the requirements of fast and real-time fault detection.