To reduce the number of aircraft on ground, the electrical design engineers are interested in predicting the oil temperature of the generator during a flight. Changes on the temperature value may indicate an incorrect functioning of the generator. An abnormal behavior can be identified by using machine learning algorithms that predict the generator oil temperature and are trained on flights free from any anomalies. The predictions resulting from the algorithm can then be compared to the observed values, here the sensor data collected from the aircraft during flight. If the observed value is far from the predicted value, a failure warning is raised and a maintenance action shall be performed.
In this paper, we build a digital twin of the electrical generator which predicts the oil generator temperature at a given time thanks to the history of features. We compare several machine learning procedures and the most promising procedure is chosen to predict the generator oil temperature.
The digital twin is tested by using real flight data containing generator failures and it is verified that the algorithm is able to detect an anomaly prior to the failure events (early failure detection).
1st Mosharaka International Conference on Emerging Applications of Electrical Engineering (MIC-ElectricApps 2020)
Congress
2020 Global Congress on Electrical Engineering (GC-ElecEng 2020), 4-6 September 2020, Valencia, Spain
Pages
27-32
Topics
Artificial Intelligence Tools Intelligent Data Analysis
ISSN
2227-331X
DOI
BibTeX
@inproceedings{1144ElecEng2020,
title={Anomaly detection for aircraft electrical generator using machine learning in a
functional data framework},
author={Fériel Boulfani, and Xavier Gendre, and Anne Ruiz-Gazen, and Martina Salvignol},
booktitle={2020 Global Congress on Electrical Engineering (GC-ElecEng 2020)},
year={2020},
pages={27-32},
doi={}},
organization={Mosharaka for Research and Studies}
}
