Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/3240
Title: AI-based low computational power actuator/sensor fault detection applied on a MAGLEV suspension
Authors: Michail, Konstantinos 
Tzafestas, Spyros G. 
Zolotas, Argyrios C. 
Deliparaschos, Kyriakos M. 
Keywords: Artificial intelligence
Electromagnetic actuators
Fault diagnosis
Magnetic levitation
Neural nets
Observers
Suspensions (mechanical components)
Issue Date: 2013
Publisher: IEEE Xplore
Source: Control & Automation (MED), 2013 21st Mediterranean Conference on , vol., no., pp.1127,1132, 25-28 June 2013
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.
URI: http://ktisis.cut.ac.cy/jspui/handle/10488/3240
ISBN: 978-1-4799-0995-7
DOI: 10.1109/MED.2013.6608862
Rights: IEEE Xplore
Appears in Collections:Δημοσιεύσεις σε συνέδρια/Conference papers

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