v Experimental Development of a Health Monitoring Method for Electro-Mechanical Actuators of Flight Control Primary Surfaces in More Electric Aircrafts · Mirko Mazzoleni

# Experimental Development of a Health Monitoring Method for Electro-Mechanical Actuators of Flight Control Primary Surfaces in More Electric Aircrafts

### Abstract

This paper presents a health monitoring approach for Electro-Mechanical Actuators (EMA). We define four different indicators to continuously evaluate the health state of the system. The four indicators are computed by leveraging the output from a Statistical Process Monitoring (SPM) method based on multivariate statistics, such as the Hotelling’s $$T^{2}$$ statistic and the $$Q$$ statistic. SPM approaches give a dichotomous answer, i.e. the presence/absence of a fault. In this work, we propose four ways to compute a continuous indicator starting from the discrete SPM output, that is better suited for health monitoring. We test the approach using a dataset collected from a large experimental campaign on a 1:1 scale EMA for primary flight controls of small aircrafts, that led to EMA failure. Results show the effectiveness of the method. [Paper, Code]

#### Reference

M. Mazzoleni, F. Previdi, M. Scandella and G. Pispola, "Experimental Development of a Health Monitoring Method for Electro-Mechanical Actuators of Flight Control Primary Surfaces in More Electric Aircrafts", IEEE Access, 2019. , doi: 10.1109/ACCESS.2019.2948781 , ISSN: 2169-3536, vol. 7, pp. 153618-153634.

#### Bibtex

@article{8878102,
author={M. {Mazzoleni} and F. {Previdi} and M. {Scandella} and G. {Pispola}},
journal={IEEE Access},
title={Experimental Development of a Health Monitoring Method for Electro-Mechanical Actuators of Flight Control Primary Surfaces in More Electric Aircrafts},
year={2019},
volume={7},
number={},
pages={153618-153634},
keywords={Actuators;Monitoring;Aircraft;Fault diagnosis;Aerospace control;Fault detection;Safety;Actuators;aerospace components;aerospace safety;condition monitoring;electromechanical systems;fault detection;predictive maintenance;statistical process monitoring},
doi={10.1109/ACCESS.2019.2948781},
ISSN={},
month={},}