Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/8112
Title: Sensor fault detection with low computational cost : a proposed neural network-based control scheme
Authors: Michail, Konstantinos 
Deliparaschos, Kyriakos M. 
Michail, Konstantinos 
Keywords: Kalman filters
Artificial intelligence
Electromagnetic devices
Fault diagnosis
Magnetic levitation
Neurocontrollers
Issue Date: 2012
Publisher: IEEE
Source: IEEE 17th Conference on Emerging Technologies & Factory Automation (ETFA), 17-21 Sept. 2012
Abstract: The paper describes a low computational power method for detecting sensor faults. A typical fault detection unit for multiple sensor fault detection with modelbased approaches, requires a bank of estimators. The estimators can be either observer or artificial intelligence based. The proposed control scheme uses an artificial intelligence approach for the development of the fault detection unit abbreviated as ‘i-FD’. In contrast with the bank-estimators approach the proposed i-FD unit is using only one estimator for multiple sensor fault detection. The efficacy of the scheme is tested on an Electro-Magnetic Suspension (EMS) system and compared with a bank of Kalman estimators in simulation environment.
URI: http://ktisis.cut.ac.cy/handle/10488/8112
ISSN: 1946-0740
1946-0740
DOI: 10.1109/ETFA.2012.6489628
Rights: © Copyright 2013 IEEE - All rights reserved.
Appears in Collections:Δημοσιεύσεις σε συνέδρια/Conference papers

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