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https://hdl.handle.net/20.500.14279/4206
Τίτλος: | AI-based low computational power actuator/sensor fault detection applied on a MAGLEV suspension | Συγγραφείς: | Michail, Konstantinos Tzafestas, Spyros G. Zolotas, Argyrios C. Deliparaschos, Kyriakos M. |
Major Field of Science: | Engineering and Technology | Field Category: | Mechanical Engineering | Λέξεις-κλειδιά: | Artificial intelligence;Electromagnetic actuators;Fault diagnosis;Magnetic levitation;Neural nets;Observers;Suspensions (mechanical components) | Ημερομηνία Έκδοσης: | 2013 | Πηγή: | 21st Mediterranean Conference on Control & Automation (MED) , pp.1127,1132, 25-28 June 2013 | Link: | http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6608862&isnumber=6608682 | Conference: | Mediterranean Conference on Control & Automation (MED) | Περίληψη: | 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. | ISBN: | 978-1-4799-0995-7 | DOI: | 10.1109/MED.2013.6608862 | Rights: | IEEE Xplore | Type: | Conference Papers | Affiliation: | Cyprus University of Technology National and Kapodistrian University of Athens University of Sussex |
Publication Type: | Peer Reviewed |
Εμφανίζεται στις συλλογές: | Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation |
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