The paper aims to propose a new prognostic procedure centered on the characterization of the state of health of an electrohydraulic servomechanism typically used in aircraft primary flight controls. This approach is based on the innovative use of a model based fault detection and identification method (FDI) that applies artificial neural network in order to identify the actual state of wear of the actuator. The considered case study concerns an electrohydraulic actuator with a flapper-nozzle servo-valve whose progressive faults taken into consideration are: the clogging of the first stage of the flapper-nozzle valve, and the progressive gain loss of the torque motor. By means of the extraction of data from the system responses, under different extent of damage, multiple neural networks have been trained. Such networks have been integrated to create the prognostic method that has finally been tested under different conditions.

Prognostics of onboard electrohydraulic servomechanisms: Proposal of a novel model-based fault detection neural technique / Dalla Vedova, Matteo D. L.; Cerqua, Gianluca; Maggiore, Paolo. - ELETTRONICO. - (2017), pp. 99-106. ((Intervento presentato al convegno 2017 2nd International Conference on System Reliability and Safety tenutosi a Milan, Italy nel 20-22 Dec. 2017 [10.1109/ICSRS.2017.8272803].

Prognostics of onboard electrohydraulic servomechanisms: Proposal of a novel model-based fault detection neural technique

Dalla Vedova, Matteo D. L.;Cerqua, Gianluca;Maggiore, Paolo
2017

Abstract

The paper aims to propose a new prognostic procedure centered on the characterization of the state of health of an electrohydraulic servomechanism typically used in aircraft primary flight controls. This approach is based on the innovative use of a model based fault detection and identification method (FDI) that applies artificial neural network in order to identify the actual state of wear of the actuator. The considered case study concerns an electrohydraulic actuator with a flapper-nozzle servo-valve whose progressive faults taken into consideration are: the clogging of the first stage of the flapper-nozzle valve, and the progressive gain loss of the torque motor. By means of the extraction of data from the system responses, under different extent of damage, multiple neural networks have been trained. Such networks have been integrated to create the prognostic method that has finally been tested under different conditions.
978-1-5386-3322-9
File in questo prodotto:
File Dimensione Formato  
08272803 - Final Paper IEEE.pdf

non disponibili

Descrizione: Versione finale del paper disponibile online
Tipologia: 2a Post-print versione editoriale / Version of Record
Licenza: Non Pubblico - Accesso privato/ristretto
Dimensione 835.08 kB
Formato Adobe PDF
835.08 kB Adobe PDF   Visualizza/Apri   Richiedi una copia
ICSRS_2017 Dalla_Vedova FINAL2.pdf

accesso aperto

Descrizione: Postprint Draft
Tipologia: 2. Post-print / Author's Accepted Manuscript
Licenza: PUBBLICO - Tutti i diritti riservati
Dimensione 1.05 MB
Formato Adobe PDF
1.05 MB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

Caricamento pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11583/2704230
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo