AI-based actuator/sensor fault detection with low computational cost for industrial applications

Michail, Konstantinos, Deliparaschos, Kyriakos M., Tzafestas, Spyros G. and Zolotas, Argyrios C. (2016) AI-based actuator/sensor fault detection with low computational cost for industrial applications. IEEE Transactions on Control Systems Technology, 24 (1). pp. 293-301. ISSN 1063-6536

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A low computational cost method is proposed for detecting actuator/sensor faults. Typical model-based fault detection units for multiple sensor faults, require a bank of estimators (i.e., conventional Kalman estimators or artificial intelligence based ones). The proposed fault detection scheme uses an artificial intelligence approach for developing of a low computational power fault detection unit abbreviated as ‘iFD’. In contrast to the bank-of-estimators approach, the proposed iFD unit employs a single estimator for multiple actuator/sensor fault detection. The efficacy of the proposed fault detection scheme is illustrated through a rigorous analysis of the results for a number of sensor fault scenarios on an electromagnetic suspension system.

Keywords:Fault tolerant control, Actuator/sensor fault detection, Reconfigurable control, Loop-shaping robust control design, Electromagnetic suspension, Maglev trains, Neural networks, Artificial intelligence, bmjtype, bmjgoldcheck, NotOAChecked
Subjects:H Engineering > H660 Control Systems
Divisions:College of Science > School of Engineering
ID Code:17260
Deposited On:24 Apr 2015 12:07

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