This paper presents a research activity which is being performed with the aim of contributing to the development of an effective prognostics and health management (PHM) system for the electrohydraulic servoactuators of primary flight controls. One of the key objectives of the research work is to develop a PHM system without adding new sensors, but exploiting in the most appropriate way all information already available in the present EHSAs. This will extend the possibility of providing the PHM capability to the EHSAs of legacy aircraft and not limit the applicability of PHM to new platforms. The PHM system which is being developed is based on a neural network approach in which the health indexes defining the EHSA significant features are processed by appropriate algorithms. The neural network acts as a reasoner that performs data fusion eventually detecting and classifying an anomalous condition, if it exists. The paper outlines the research work, describing approach, methodology, structure of the PHM system, results of test cases and assessment of its effectiveness, with the evaluation of the probability of missed failures and false alarms.
Development of a Prognostic and Health Management System for Electro ydraulic Servoactuators of Primary Flight Controls / Jacazio, Giovanni; Mornacchi, Andrea; Sorli, Massimo. - ELETTRONICO. - (2015), pp. 13-22. (Intervento presentato al convegno AST 2015 tenutosi a Hamburg, Germany nel February 24-25, 2015).
Development of a Prognostic and Health Management System for Electro ydraulic Servoactuators of Primary Flight Controls
JACAZIO, Giovanni;MORNACCHI, ANDREA;SORLI, Massimo
2015
Abstract
This paper presents a research activity which is being performed with the aim of contributing to the development of an effective prognostics and health management (PHM) system for the electrohydraulic servoactuators of primary flight controls. One of the key objectives of the research work is to develop a PHM system without adding new sensors, but exploiting in the most appropriate way all information already available in the present EHSAs. This will extend the possibility of providing the PHM capability to the EHSAs of legacy aircraft and not limit the applicability of PHM to new platforms. The PHM system which is being developed is based on a neural network approach in which the health indexes defining the EHSA significant features are processed by appropriate algorithms. The neural network acts as a reasoner that performs data fusion eventually detecting and classifying an anomalous condition, if it exists. The paper outlines the research work, describing approach, methodology, structure of the PHM system, results of test cases and assessment of its effectiveness, with the evaluation of the probability of missed failures and false alarms.Pubblicazioni consigliate
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https://hdl.handle.net/11583/2599758
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