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Optimal and near-optimal algorithms for multiple fault diagnosis with unreliable tests

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4 Author(s)
Shakeri, M. ; Qualtech Syst. Inc., Mansfield, CT, USA ; Raghavan, V. ; Pattipati, K.R. ; Patterson-Hine, A.

In this paper, we consider the problem of constructing optimal and near-optimal multiple fault diagnosis (MFD) in bipartite systems with unreliable (imperfect) tests. It is known that exact computation of conditional probabilities for multiple fault diagnosis is NP-hard. The novel feature of our diagnostic algorithms is the use of Lagrangian relaxation and sub-gradient optimization methods to provide: (1) near optimal solutions for the MFD problem, and (2) upper bounds for an optimal branch-and-bound algorithm. The proposed method is illustrated using several examples. Computational results indicate that: (1) our algorithm has superior computational performance to the existing algorithms, (2) the near optimal algorithm generates the most likely candidates with a very high accuracy, and (3) our algorithm can find the most likely candidates in systems with as many as 1000 faults

Published in:

AUTOTESTCON '96, Test Technology and Commercialization. Conference Record

Date of Conference:

16-19 Sep 1996