基于故障树的远程故障诊断任务分解和决策方法

Research on Remote Fault Diagnosis Task Decomposition and Decision Approach

摘要:

远程故障诊断对提高飞机飞行安全、降低诊断和维护成本、建立更好的飞机维护环境具有重要作用,在远程故障诊断过程中,面对复杂的诊断任务和冗余、不确定的决策如何做出合理的任务分解和最优的诊断决策是个重要的研究课题。以提高诊断效率和可靠性,本文提出了一种基于故障树模型的任务分解与决策融合方法。首先,描述了多资源远程诊断任务分解问题；其次,建立了基于故障树最小割集的诊断任务分解机制；最后,提出了基于D-S证据理论的决策级信息融合方法,并通过实际算例验证了任务分解和决策方法的可行性。

Abstract:

Remote fault diagnosis (RFD) is of great importance in improving the flight safety, reducing cost of diagnosis and maintenance and establishing a better environment for aircraft maintenance. During the process of remote fault diagnosis, how to make reasonable task decomposition towards complex diagnosis task and how to make the optimal diagnosis decision on redundant or uncertain decisions are vital research issues. In this paper, we present methodologies to improve the diagnosis efficiency and reliability. First, RFD Task Decomposition of multi-resource is descripted in this paper. Then fault tree based minimal cut set is put forward to decompose the complicate RFD task into some smaller executable subtasks. Finally we present decision-level information fusion approach based on Dempster-Shafer (D-S) evidential theory. The examples analysis are given to demonstrate the effectiveness of our approach.