Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
699034 | Control Engineering Practice | 2015 | 17 Pages |
Abstract
Quick, robust fault diagnosis is critical to ensuring safe operation of complex engineering systems. A fault detection, isolation, and identification framework is developed for three separate diagnosis algorithms: the first using global model; the second using minimal submodels, which allows the approach to scale easily; and the third using both the global model and minimal submodels, combining the strengths of the first two. The diagnosis framework is applied to the Advanced Diagnostics and Prognostics Testbed that functionally represents spacecraft electrical power distribution systems. The practical implementation of these algorithms is described, and their diagnosis performance using real data is compared.
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Authors
Matthew J. Daigle, Indranil Roychoudhury, Anibal Bregon,