Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/8470
Title: AI-based actuator/sensor fault detection with low computational cost for industrial applications
Authors: Michail, Konstantinos 
Deliparaschos, Kyriakos M. 
Tzafestas, Spyros G. 
Zolotas, Argyrios C. 
Keywords: Actuator/sensor fault detection (FD);Artificial intelligence (AI);Electromagnetic suspension (EMS);Fault tolerant control (FTC);Loop-shaping robust control design;Maglev trains;Neural networks (NNs);Reconfigurable control
Category: Electrical Engineering - Electronic Engineering - Information Engineering
Field: Engineering and Technology
Issue Date: 2016
Publisher: IEEE
Source: IEEE Transactions on Control Systems Technology, 2016, Volume 24, Number 1, Pages 293-301
Abstract: A low computational cost method is proposed for detecting actuator/sensor faults. Typical model-based fault detection (FD) units for multiple sensor faults require a bank of estimators [i.e., conventional Kalman estimators or artificial intelligence (AI)-based ones]. The proposed FD scheme uses an AI approach for developing of a low computational power FD unit abbreviated as iFD. In contrast to the bank-of-estimators approach, the proposed iFD unit employs a single estimator for multiple actuator/sensor FD. The efficacy of the proposed FD scheme is illustrated through a rigorous analysis of the results for a number of sensor fault scenarios on an electromagnetic suspension system.
URI: http://ktisis.cut.ac.cy/handle/10488/8470
ISSN: 1063-6536
DOI: 10.1109/TCST.2015.2422794
Rights: © IEEE
Type: Article
Appears in Collections:Άρθρα/Articles

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