Prognostics and Health Management of an Electro-Hydraulic Servo Actuator

Andrea Mornacchi, George Vachtsevanos, and Giovanni Jacazio
Submission Type: 
Full Paper
phmc_15_075.pdf402.7 KBSeptember 1, 2015 - 8:21am

Electro-Hydraulic Servo Actuators (EHSA) is the principal technology used for primary flight control in new aircrafts and legacy platforms. The development of Prognostic and Health Management technologies and their application to EHSA systems is of great interest in both the aerospace industry and the air fleet operators.
This paper presents the results of an ongoing research activity focused on the development of a PHM system for fly-by-wire primary flight EHSA. One of the key features of the research is the implementation of a PHM system without the addition of new sensors, taking advantage of sensing and information already available. This choice allows extending the PHM capability to the EHSAs of legacy platforms and not only to new aircrafts. The enabling technologies borrow from the area of Bayesian estimation theory and specifically particle filtering and the information acquired from EHSA during pre-flight check is processed by appropriate algorithms in order to obtain relevant features, detect the degradation and estimate the Remaining Useful Life (RUL). The results are evaluated through appropriate metrics in order to assess the performance and effectiveness of the implemented PHM system.
The major objective of this contribution is to develop an innovative fault diagnosis and failure prognosis framework for critical aircraft components that integrates effectively mathematically rigorous and validated signal processing, feature extraction, diagnostic and prognostic algorithms with novel uncertainty representation and management tools in a platform that is computationally efficient and ready to be transitioned on-board an aircraft.

Publication Year: 
Publication Volume: 
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Submission Keywords: 
anomaly detection
failure prognosis
particle filtering
primary flight command
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis
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