Progressive failures affecting onboard electromechanical actuators (EMA), especially if related to primary flight commands, could be a critical issue for the aircraft reliability and, in the worst cases, could compromise its safety. In the last years strong interest is expected by the development of prognostic algorithms able to provide an early identification of the precursors of EMA progressive failures. In this work authors proposes a new prognostic method based on two artificial neural networks (ANN), a basic and an enhanced feedforward neural network, performing the fault detection and identification of two critical progressive faults often affecting the EMA brushless motor (i.e. turn-to-turn short circuit of a stator coil and rotor static eccentricity); in order to identify a suitable data set able to guarantee an affordable ANN classification, the said failures precursors are properly pre-processed by a Discrete Wavelet Transform, extracting several features used as input of the proposed prognostic algorithm.

New prognostic neural method by discrete wavelet transforms for electromechanical flight controls affected by progressive faults / Dalla Vedova, Matteo D. L.; Lampariello, Nicola; Maggiore, Paolo. - In: MATEC WEB OF CONFERENCES. - ISSN 2261-236X. - ELETTRONICO. - 233:(2018), p. 00017. ((Intervento presentato al convegno 8th EASN-CEAS International Workshop on Manufacturing for Growth and Innovation tenutosi a gbr nel 2018 [10.1051/matecconf/201823300017].

New prognostic neural method by discrete wavelet transforms for electromechanical flight controls affected by progressive faults

Dalla Vedova, Matteo D. L.;Lampariello, Nicola;Maggiore, Paolo
2018

Abstract

Progressive failures affecting onboard electromechanical actuators (EMA), especially if related to primary flight commands, could be a critical issue for the aircraft reliability and, in the worst cases, could compromise its safety. In the last years strong interest is expected by the development of prognostic algorithms able to provide an early identification of the precursors of EMA progressive failures. In this work authors proposes a new prognostic method based on two artificial neural networks (ANN), a basic and an enhanced feedforward neural network, performing the fault detection and identification of two critical progressive faults often affecting the EMA brushless motor (i.e. turn-to-turn short circuit of a stator coil and rotor static eccentricity); in order to identify a suitable data set able to guarantee an affordable ANN classification, the said failures precursors are properly pre-processed by a Discrete Wavelet Transform, extracting several features used as input of the proposed prognostic algorithm.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11583/2729336
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