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Michail, Konstantinos, Deliparaschos, Kyriakos M, Tzafestas, Spyros G and Zolotas, Argyrios C (2013) AI-based low computational power actuator/sensor fault detection applied on a MAGLEV suspension. In: Control Automation (MED), 2013 21st Mediterranean Conference on, 25-28 June 2013, Platanias, Chania - Crete, Greece.
Full text not available from this repository.Abstract
A low computational power method is proposed for detecting actuators/sensors faults. Typical model-based fault detection units for multiple sensor faults, require a bank of observers (these can be either conventional observers of artificial intelligence based). The proposed control scheme uses an artificial intelligence approach for the development of the fault detection unit abbreviated as ‘iFD’. In contrast with the bank-of-estimators approach, the proposed iFD unit employs a single estimator for multiple sensor fault detection. The efficacy of the scheme is illustrated on an Electromagnetic Suspension system example with a number of sensor fault scenaria.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Keywords: | Acceleration;Actuators;Artificial neural networks;Energy management;Fault detection;Suspensions;Training |
| Schools and Departments: | School of Engineering and Informatics > Engineering and Design |
| Subjects: | T Technology > TJ Mechanical engineering and machinery > TJ0212 Control engineering systems. Automatic machinery (General) |
| Depositing User: | Argyrios Zolotas |
| Date Deposited: | 03 Oct 2013 08:03 |
| Last Modified: | 03 Oct 2013 08:08 |
| URI: | http://sro.sussex.ac.uk/id/eprint/46512 |