AI-based actuator/sensor fault detection with low computational cost for industrial applications - Michail, Konstantinos; Deliparaschos, Kyriakos M.; Tzafestas, Spyros G.; Zolotas, Argyrios C. (My University, 2016)
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.
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