Fault isolation for large scale discrete-time systems based on implicit set representation

Franco Blanchini, Daniele Casagrande, Giulia Giordano, Stefano Miani, Sorin Olaru, V. Reppa

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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To detect faults in a system we can adopt an observer, designed for the healthy system, and monitor the discrepancy between actual and expected behaviour of the residual (difference between the system output and its estimate). To isolate faults, we can compute the invariant sets associated with each fault, and their projection in the residual space (limit set): faults can be isolated if the associated limit sets are separated when a (constant) test input is applied. However, the explicit computation of limit sets can be hard even for low-dimensional systems. As a main contribution, we show that, by adopting an implicit representation of limit sets, very efficient procedures can be used to solve the problem, based on convex quadratic programming or linear programming. Simulations show that the approach is effective in solving even large dimensional problems, which makes it suitable for large- scale networked systems.
Original languageEnglish
Title of host publicationProceedings 2018 European Control Conference (ECC2018)
Place of PublicationPiscataway, NJ, USA
ISBN (Print)978-3-9524-2699-9
Publication statusPublished - 2018
Event16th European Control Conference, ECC 2018 - Limassol, Cyprus
Duration: 12 Jun 201815 Jun 2018


Conference16th European Control Conference, ECC 2018
Abbreviated titleECC 2018
Internet address

Bibliographical note

Accepted Author Manuscript


  • Fault detection
  • Discrete-time systems
  • Monitoring
  • Linear programming
  • Observers
  • Quadratic programming
  • Real-time systems


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