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Innovative Actuator Fault Identification Based on Back Electromotive Force Reconstruction

Department of Aerospace and Mechanical Engineering (DIMEAS), Politecnico di Torino, 10129 Turin, Italy
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Actuators 2020, 9(3), 50; https://doi.org/10.3390/act9030050
Received: 21 May 2020 / Revised: 27 June 2020 / Accepted: 1 July 2020 / Published: 3 July 2020
(This article belongs to the Section Aircraft Actuators)
The ever increasing adoption of electrical power as secondary form of on-board power is leading to an increase in the usage of electromechanical actuators (EMAs). Thus, in order to maintain an acceptable level of safety and reliability, innovative prognostics and diagnostics methodologies are needed to prevent performance degradation and/or faults propagation. Furthermore, the use of effective prognostics methodologies carries several benefits, including improved maintenance schedule capability and relative cost decrease, better knowledge of systems health status and performance estimation. In this work, a novel, real-time approach to EMAs prognostics is proposed. The reconstructed back electromotive force (back-EMF), determined using a virtual sensor approach, is sampled and then used to train an artificial neural network (ANN) in order to evaluate the current system status and to detect possible coils partial shorts and rotor imbalances. View Full-Text
Keywords: prognostics; back-EMF coefficient; virtual sensor; artificial neural network; electromechanical actuators prognostics; back-EMF coefficient; virtual sensor; artificial neural network; electromechanical actuators
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MDPI and ACS Style

Quattrocchi, G.; Berri, P.C.; Dalla Vedova, M.D.L.; Maggiore, P. Innovative Actuator Fault Identification Based on Back Electromotive Force Reconstruction. Actuators 2020, 9, 50. https://doi.org/10.3390/act9030050

AMA Style

Quattrocchi G, Berri PC, Dalla Vedova MDL, Maggiore P. Innovative Actuator Fault Identification Based on Back Electromotive Force Reconstruction. Actuators. 2020; 9(3):50. https://doi.org/10.3390/act9030050

Chicago/Turabian Style

Quattrocchi, Gaetano, Pier C. Berri, Matteo D. L. Dalla Vedova, and Paolo Maggiore. 2020. "Innovative Actuator Fault Identification Based on Back Electromotive Force Reconstruction" Actuators 9, no. 3: 50. https://doi.org/10.3390/act9030050

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