Multiple Fault Diagnostic Strategy for Redundant System
It is difficult to diagnose the faults, especially multiple faults, in redundant systems by traditional diagnostic strategies. So the problem of multiple fault diagnostic strategy for redundant system was researched in this paper. Firstly, the typical characters of multiple faults (minimal faults) were analyzed, and the problem was formulated. Secondly, a pair of two-tuples were applied to denote the possible and impossible diagnostic conclusion at different diagnostic stages respectively, and a multiple fault diagnostic inference engine was constructed based on Boolean logic. The inference engine can determine the system diagnostic conclusions after executing each test, and determine whether a repair action was needed, and further determine whether a next test was needed. Thirdly, a method determining the next best test was presented. Based on the proposed inference engine and test determining method, a multiple fault diagnostic strategy was constructed. Finally, a simulation case and a certain flight control system were applied to illustrate the proposed diagnostic strategy. The simulation and practical data computational results show that the presented diagnostic strategy can diagnose multiple faults in redundant systems effectively and it is of certain application value.
How to Cite
 Davis R. Retrospective on diagnostic reasoning based on structure and behavior [J]. Artificial Intelligence, 1993,59: 149-157.
 Tu F, Pattipati K R, Deb S, et al. Computationally efficient algorithms for multiple fault diagnosis in large graph-based systems[J]. IEEE Transactions on Systems, Man and Cybernetics, 2003 33(1):73-85.
 Long Bing, Jiang Xing-wei, Song Zheng-ji. Study on multiple fault diagnostic technique for aerospace craft based on multi-signal model[J]. Journal of astronavigation, 2004, 25(5):591-594.[in chinese]
 Stefano Chessa, Paolo Santi. Operative Diagnosis of Graph-Based Systems with Multiple Faults.[J]. IEEE Transactions on Systems, Man and Cybernetics, 2001, 31(2):112-119.
 Kleer J D. Diagnosing Multiple Faults. Artificial Intelligence. 1987, 32:97-130.
 Shakeri M, Raghavan V, Pattipati K, et al. Sequential testing algorithms for multiple fault isolation[J]. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, 2000, 30(1):1-14.
 Shakeri M, Pattipati K R, Raghavan V, et al. Optimal and Near-Optimal Algorithms for Multiple Fault Diagnosis with Unreliable Tests [J]. IEEE trans on SMC,1998:431-440.
 Doyle S A, Dugan J B, Patterson-Hine A. A quantitative analysis of the F18 flight control system[C]. American Institute of Aeronautics and Astronautics Computing in Aerospace 9 Conference proceedings, 1993:668-675.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.