Several approaches can be employed in prognostics, to detect incipient failures of primary flight command electromechanical actuators (EMA), caused by progressive wear. The development of a prognostic algorithm capable of identifying the precursors of an electromechanical actuator failure is beneficial for the anticipation of the incoming faults: a correct interpretation of the fault degradation pattern can trig an early alert of the maintenance crew, who can properly schedule the servomechanism replacement. The research presented in this paper proposes a fault detection and identification technique, based on approaches derived from optimization methods, able to identify symptoms of EMA degradation before the actual exhibition of the anomalous behavior; in particular, the authors’ work analyses the effects due to progressive backlashes acting on the mechanical transmission and evaluates the effectiveness of the proposed approach to correctly identify these faults. An experimental test bench was developed: results show that the method exhibit adequate robustness and a high degree of confidence in the ability to early identify an eventual fault, minimizing the risk of false alarms or unrecognized failures.

Effects of Mechanical Backlash on Linear Electromechanical Actuators: A Fault Identification Method based on the Simulated Annealing Algorithm / DALLA VEDOVA, MATTEO DAVIDE LORENZO; Maggiore, Paolo; Pace, Lorenzo. - STAMPA. - 47:(2015), pp. 151-156. ((Intervento presentato al convegno 6th International Conference on Theoretical and Applied Mechanics (TAM '15) tenutosi a Salerno nel June, 27-29. 2015.

Effects of Mechanical Backlash on Linear Electromechanical Actuators: A Fault Identification Method based on the Simulated Annealing Algorithm

DALLA VEDOVA, MATTEO DAVIDE LORENZO;MAGGIORE, Paolo;PACE, LORENZO
2015

Abstract

Several approaches can be employed in prognostics, to detect incipient failures of primary flight command electromechanical actuators (EMA), caused by progressive wear. The development of a prognostic algorithm capable of identifying the precursors of an electromechanical actuator failure is beneficial for the anticipation of the incoming faults: a correct interpretation of the fault degradation pattern can trig an early alert of the maintenance crew, who can properly schedule the servomechanism replacement. The research presented in this paper proposes a fault detection and identification technique, based on approaches derived from optimization methods, able to identify symptoms of EMA degradation before the actual exhibition of the anomalous behavior; in particular, the authors’ work analyses the effects due to progressive backlashes acting on the mechanical transmission and evaluates the effectiveness of the proposed approach to correctly identify these faults. An experimental test bench was developed: results show that the method exhibit adequate robustness and a high degree of confidence in the ability to early identify an eventual fault, minimizing the risk of false alarms or unrecognized failures.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11583/2615051
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