http://www.cnr.it/ontology/cnr/individuo/prodotto/ID36924
An approach to model-based fault detection in industrial measurement systems with application to engine test benches (Articolo in rivista)
- Type
- Label
- An approach to model-based fault detection in industrial measurement systems with application to engine test benches (Articolo in rivista) (literal)
- Anno
- 2006-01-01T00:00:00+01:00 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
- 10.1088/0957-0233/17/7/020 (literal)
- Alternative label
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
- Plamen Angelov 1; Veniero Giglio 2; Carlos Guardiola 3; Edwin Lughofer 4; José Manuel Lujan 3 (literal)
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- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
- Rivista
- Note
- ISI Web of Science (WOS) (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
- 1) Department of Communication Systems, Lancaster University, UK;
2) Istituto Motori, CNR, ITtaly; 3) CMT Motores Termicos, Universidad Politecnica de Valencia, Spain; 4) Department Knowledge-Based Mathematical Systems, Johannes Kepler University of Linz, Austria. (literal)
- Titolo
- An approach to model-based fault detection in industrial measurement systems with application to engine test benches (literal)
- Abstract
- An approach to fault detection (FD) in industrial measurement systems is
proposed in this paper which includes an identification strategy for early
detection of the appearance of a fault. This approach is model based, i.e.
nominal models are used which represent the fault-free state of the on-line
measured process. This approach is also suitable for off-line FD. The framework that combines FD with isolation and correction (FDIC) is outlined in this paper. The proposed approach is characterized by automatic
threshold determination, ability to analyse local properties of the models,
and aggregation of different fault detection statements. The nominal models
are built using data-driven and hybrid approaches, combining first principle models with on-line data-driven techniques. At the same time the models are transparent and interpretable. This novel approach is then verified on a number of real and simulated data sets of car engine test benches (both gasolineAlfa Romeo JTS, and dieselCaterpillar). It is demonstrated that the approach can work effectively in real industrial measurement systems with data of large dimensions in both on-line and off-line modes. (literal)
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