Please use this identifier to cite or link to this item:
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)
Category: Mechanical Engineering
Field: Engineering and Technology
Issue Date: 2013
Publisher: IEEE Xplore
Source: 21st Mediterranean Conference on Control & Automation (MED) , pp.1127,1132, 25-28 June 2013
Conference: Mediterranean Conference on Control & Automation (MED) 
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.
ISBN: 978-1-4799-0995-7
DOI: 10.1109/MED.2013.6608862
Rights: IEEE Xplore
Type: Conference Papers
Appears in Collections:Δημοσιεύσεις σε συνέδρια/Conference papers

Files in This Item:
File Description SizeFormat
AI-based.pdf500.74 kBAdobe PDFView/Open
Show full item record

Citations 20

checked on Dec 12, 2018

Page view(s) 50

Last Week
Last month
checked on Jun 12, 2019

Download(s) 50

checked on Jun 12, 2019

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.