NASA Logo

NTRS

NTRS - NASA Technical Reports Server

Back to Results
Algorithms for Multiple Fault Diagnosis With Unreliable TestsIn 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 subgradient 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 (approximately three orders of magnitude improvement), (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.
Document ID
19980096379
Acquisition Source
Ames Research Center
Document Type
Other
Authors
Shakeri, Mojdeh
(Qualtech Systems, Inc. Mansfield, CT United States)
Raghavan, Vijaya
(Qualtech Systems, Inc. Mansfield, CT United States)
Pattipati, Krishna R.
(Connecticut Univ. Storrs, CT United States)
Patterson-Hine, Ann
(NASA Ames Research Center Moffett Field, CA United States)
Date Acquired
August 18, 2013
Publication Date
May 1, 1997
Publication Information
Publication: Multiple Fault Isolation in Redundant Systems
Subject Category
Quality Assurance And Reliability
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.
Document Inquiry

Available Downloads

There are no available downloads for this record.
No Preview Available