Abstract
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 language | English |
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Title of host publication | Proceedings 2018 European Control Conference (ECC2018) |
Place of Publication | Piscataway, NJ, USA |
Publisher | IEEE |
Pages | 685-690 |
ISBN (Print) | 978-3-9524-2699-9 |
DOIs | |
Publication status | Published - 2018 |
Event | 16th European Control Conference, ECC 2018 - Limassol, Cyprus Duration: 12 Jun 2018 → 15 Jun 2018 http://www.ecc18.eu/ |
Conference
Conference | 16th European Control Conference, ECC 2018 |
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Abbreviated title | ECC 2018 |
Country/Territory | Cyprus |
City | Limassol |
Period | 12/06/18 → 15/06/18 |
Internet address |
Bibliographical note
Accepted Author ManuscriptKeywords
- Fault detection
- Discrete-time systems
- Monitoring
- Linear programming
- Observers
- Quadratic programming
- Real-time systems