Observers data only fault detection
2009 (English)In: 7th IFAC International Symposium on Fault Detection, Supervision and Safety of Technical Systems, SAFEPROCESS'09, 2009, 959-964 p.Conference paper (Refereed)
Most fault detection algorithms are based on residuals, i.e. the difference between a measured signal and the corresponding model based prediction. However, in many more advanced sensors the raw measurements are internally processed before refined information is provided to the user. The contribution of this paper is to study the problem of fault detection when only the state estimate from an observer/Kalman filter is available and not the direct measured quantities. The idea is to look at an extended state space model where the true states and the observer states are combined. This extended model is then used to generate residuals viewing the observer outputs as measurements. Results for fault observability of such extended models are given. The approach is rather straightforward in case the internal structure of the observer is exactly known. For the Kalman filter this corresponds to knowing the observer gain. If this is not the case certain model approximations can be done to generate a simplified model to be used for standard fault detection. The corresponding methods are evaluated on a DC motor example. The next step is a real data robotics demonstrator.
Place, publisher, year, edition, pages
2009. 959-964 p.
Extended model, Fault detection algorithm, Internal structure, Measured signals, Model approximations, Model-based prediction, Observer gain, Observer output, Raw measurements, Simplified models, State estimates, State space model, DC motors, State space methods, Fault detection
IdentifiersURN: urn:nbn:se:kth:diva-55389DOI: 10.3182/20090630-4-ES-2003.0154ScopusID: 2-s2.0-79960907983OAI: oai:DiVA.org:kth-55389DiVA: diva2:471647
7th IFAC International Symposium on Fault Detection, Supervision and Safety of Technical Systems, SAFEPROCESS'09. Barcelona. 30 June 2009 - 3 July 2009
QC 20120104. Sponsors: ABB; IFAC; Generalitat de Catalunya; Universitat Duisburg Essen; Gobierno de Espana - Ministerio de Ciencia e Innovacion2012-01-312012-01-022013-09-05Bibliographically approved