Comparison of dynamic Bayesian network approaches for online diagnosis of aircraft system  

作　　者：于劲松 冯威 唐荻音 刘浩 

机构地区：[1]School of Automation Science and Electrical Engineering,Beihang University [2]Collaborative Innovation Center of Advanced Aero-Engine,Beihang University [3]Unit 93,Army 95809 of PLA

出　　处：《Journal of Central South University》2016年第11期2926-2934,共9页中南大学学报（英文版）

基　　金：Projects(2010ZD11007,20100751010)supported by Aeronautical Science Foundation of China

摘　　要：The online diagnosis for aircraft system has always been a difficult problem. This is due to time evolution of system change, uncertainty of sensor measurements, and real-time requirement of diagnostic inference. To address this problem, two dynamic Bayesian network(DBN) approaches are proposed. One approach prunes the DBN of system, and then uses particle filter(PF) for this pruned DBN(PDBN) to perform online diagnosis. The problem is that estimates from a PF tend to have high variance for small sample sets. Using large sample sets is computationally expensive. The other approach compiles the PDBN into a dynamic arithmetic circuit(DAC) using an offline procedure that is applied only once, and then uses this circuit to provide online diagnosis recursively. This approach leads to the most computational consumption in the offline procedure. The experimental results show that the DAC, compared with the PF for PDBN, not only provides more reliable online diagnosis, but also offers much faster inference.The online diagnosis for aircraft system has always been a difficult problem. This is due to time evolution of system change, uncertainty of sensor measurements, and real-time requirement of diagnostic inference. To address this problem, two dynamic Bayesian network（DBN） approaches are proposed. One approach prunes the DBN of system, and then uses particle filter（PF） for this pruned DBN（PDBN） to perform online diagnosis. The problem is that estimates from a PF tend to have high variance for small sample sets. Using large sample sets is computationally expensive. The other approach compiles the PDBN into a dynamic arithmetic circuit（DAC） using an offline procedure that is applied only once, and then uses this circuit to provide online diagnosis recursively. This approach leads to the most computational consumption in the offline procedure. The experimental results show that the DAC, compared with the PF for PDBN, not only provides more reliable online diagnosis, but also offers much faster inference.

关 键 词：online diagnosis dynamic Bayesian network particle filter dynamic arithmetic circuit 

分 类 号：V267[航空宇航科学与技术—航空宇航制造工程]