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  • ElecEng
  • GC-ElecEng 2020
  • 29.Cnf-1144
ElecEng Congresses with Published Papers
GC-ElecEng 2020, Valencia
2
GC-ElecEng 2021, Valencia
5
Papers Published at GC-ElecEng 2020
All 2 Papers
IDAuthors and TitlePages
29.Cnf-105 Dr. Alex Vukovic
Ms. Ayat Alrjoub
Furthering Innovation in Hyper Communication Era
1-5
29.Cnf-113 Prof. Càndid Reig
Dr. Maria-Dolores Cubells-Beltran
Mr. Javio Sanchis-Muñoz
Prof. Fernando Pardo
Dr. Jose A. Boluda
Dr. Francisco Vegara
Dr. Susana Cardoso
Address Event Representation (AER) approach to resistive sensor arrays
6-9
29.Cnf-1144 Paper View Page
Title Anomaly detection for aircraft electrical generator using machine learning in a functional data framework
Authors Mrs. Fériel Boulfani, Institut de Mathématiques de Toulouse, Toulouse, France
Dr. Xavier Gendre, ISAE SUPAERO, Toulouse, France
Prof. Anne Ruiz-Gazen, Toulouse School of Economics, Toulouse, France
Mrs. Martina Salvignol, Airbus S.A.S., Toulouse, France
Abstract 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).

Track Intelligent: Intelligent Systems and Technologies
Conference 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 (Remotely), Valencia, Spain
Pages --1
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={--1},
doi={}},
organization={Mosharaka for Research and Studies} }
Paper Views 481 Paper Views Rank 156/525
Paper Downloads 164 Paper Downloads Rank 179/525
GC-ElecEng 2020 Visits: 17292||MIC-ElectricApps 2020 Visits: 10863||Intelligent Track Visits: 1995