AI-based low computational power actuator/sensor fault detection applied on a MAGLEV suspension - Michail, Konstantinos; Tzafestas, Spyros G.; Zolotas, Argyrios C.; Deliparaschos, Kyriakos M. (My University, 2013)
A low computational power method is proposed for detecting actuators/sensors faults. Typical model-based fault detection units for multiple sensor faults, require a bank of observers (these can be either conventional observers of artificial intelligence based). The proposed control scheme uses an artificial intelligence approach for the development of the fault detection unit abbreviated as `iFD'. In contrast with the bank-of-estimators approach, the proposed iFD unit employs a single estimator for multiple sensor fault detection. The efficacy of the scheme is illustrated on an Electromagnetic Suspension system example with a number of sensor fault scenaria.
Keywords: Artificial intelligence; Electromagnetic actuators; Fault diagnosis; Magnetic levitation; Neural nets; Observers; Suspensions (mechanical components)