In aviation industry, unscheduled maintenance costs may vary in a large range depending on several factors, such as specific aircraft system, operational environment, aircraft usage and maintenance policy. These costs will become more noteworthy in the next decade, due to the positive growing of worldwide fleet and the introduction of more technologically advanced aircraft. New implemented technologies will bring new challenges in the Maintenance, Repair and Overhaul (MRO) companies, both because of the rising number of new technologies and high volume of well-established devices, such as Electro-Hydraulic Servo Actuators for primary flight control. Failures in aircraft hydraulic systems deeply influence the overall failure rate and so the relative maintenance costs. For this reason, overhaul procedures for these components still represents a profitable market share for all MRO stakeholders. Innovative solutions able to facilitate maintenance operations can lead to large cost savings. This paper proposes new methodologies and features of the Intelligent Diagnostic system which is being developed in partnership with Lufthansa Technik (LHT). The implementation of this innovative procedure is built on a set of failure detection algorithms, based on Machine Learning techniques. This development requires first to bring together the results from different parallel research activities: 1. Identification of critical components from historical data; 2. Designing and testing automatic and adaptable procedure for first faults detection; 3. High-fidelity mathematical modeling of considered test units, for deeper physics analysis of possible failures; 4. Implementation of Machine Learning reasoner, able to process experimental and simulated data.

Intelligent Diagnostics for Aircraft Hydraulic Equipment / Oliver, Ritter; Gerko, Wende; Gentile, Rocco; Marino, Francesco; Bertolino, ANTONIO CARLO; Raviola, Andrea; Jacazio, Giovanni. - ELETTRONICO. - 4 No 1:(2018). ((Intervento presentato al convegno 4th European Conference of the Prognostics and Health Management Society tenutosi a Utrecht (NL) nel 3-6 July 2018.

Intelligent Diagnostics for Aircraft Hydraulic Equipment

GENTILE, ROCCO;MARINO, FRANCESCO;BERTOLINO, ANTONIO CARLO;RAVIOLA, ANDREA;Giovanni Jacazio
2018

Abstract

In aviation industry, unscheduled maintenance costs may vary in a large range depending on several factors, such as specific aircraft system, operational environment, aircraft usage and maintenance policy. These costs will become more noteworthy in the next decade, due to the positive growing of worldwide fleet and the introduction of more technologically advanced aircraft. New implemented technologies will bring new challenges in the Maintenance, Repair and Overhaul (MRO) companies, both because of the rising number of new technologies and high volume of well-established devices, such as Electro-Hydraulic Servo Actuators for primary flight control. Failures in aircraft hydraulic systems deeply influence the overall failure rate and so the relative maintenance costs. For this reason, overhaul procedures for these components still represents a profitable market share for all MRO stakeholders. Innovative solutions able to facilitate maintenance operations can lead to large cost savings. This paper proposes new methodologies and features of the Intelligent Diagnostic system which is being developed in partnership with Lufthansa Technik (LHT). The implementation of this innovative procedure is built on a set of failure detection algorithms, based on Machine Learning techniques. This development requires first to bring together the results from different parallel research activities: 1. Identification of critical components from historical data; 2. Designing and testing automatic and adaptable procedure for first faults detection; 3. High-fidelity mathematical modeling of considered test units, for deeper physics analysis of possible failures; 4. Implementation of Machine Learning reasoner, able to process experimental and simulated data.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11583/2712327
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