{ "id": "1908.03588", "version": "v1", "published": "2019-08-09T18:11:44.000Z", "updated": "2019-08-09T18:11:44.000Z", "title": "A Data-Driven and Model-Based Approach to Fault Detection and Isolation in Networked Systems", "authors": [ "Miel Sharf", "Daniel Zelazo" ], "comment": "16 pages, 6 figures", "categories": [ "eess.SY", "cs.SY", "math.OC" ], "abstract": "Fault detection and isolation is a field of engineering dealing with designing on-line protocols for systems that allow one to identify the existence of faults, pinpoint their exact location, and overcome them. We consider the case of multi-agent systems, where faults correspond to the disappearance of links in the underlying graph, simulating a communication failure between the corresponding agents. We study the case in which the agents and controllers are maximal equilibrium-independent passive (MEIP), and use the known connection between steady-states of these multi-agent systems and network optimization theory. We first study asymptotic methods of differentiating the faultless system from its faulty versions by studying their steady-state outputs. We explain how to apply the asymptotic differentiation to fault detection and isolation, with graph-theoretic guarantees on the number of faults that can be isolated, assuming the existence of a \"convergence assertion protocol\", a data-driven method of asserting that a multi-agent system converges to a conjectured limit. We then construct two data-driven model-based convergence assertion protocols. We demonstrate our results by case studies.", "revisions": [ { "version": "v1", "updated": "2019-08-09T18:11:44.000Z" } ], "analyses": { "keywords": [ "fault detection", "networked systems", "model-based approach", "multi-agent system", "data-driven model-based convergence assertion protocols" ], "note": { "typesetting": "TeX", "pages": 16, "language": "en", "license": "arXiv", "status": "editable" } } }