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|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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