Comparison of Data Depth Calculation Method for Fault Detection in Electric Signal
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-35173-0_5
Reference21 articles.
1. Aneiros, Germán, Horová, Ivana, Hušková, Marie, Vieu, Philippe: On functional data analysis and related topics. Journal of Multivariate Analysis 189, 104861 (2022)
2. Baranowski, Jerzy, Grobler-Dębska, Katarzyna, Kucharska, Edyta: Recognizing VSC DC cable fault types using bayesian functional data depth. Energies 14(18), 5893 (2021)
3. Waldemar Bauer, Adrian Dudek, and Jerzy Baranowski. Recognizing commutator motors fault from acoustics signals using bayesian functional data depth. In 2022 26th International Conference on Methods and Models in Automation and Robotics (MMAR). IEEE, aug 2022
4. Feriel Boulfani, Xavier Gendre, Anne Ruiz-Gazen, and Martina Salvignol. Anomaly detection for aircraft electrical generator using machine learning in a functional data framework. In 2020 Global Congress on Electrical Engineering (GC-ElecEng). IEEE, sep 2020
5. Christian Capezza, Fabio Centofanti, Antonio Lepore, and Biagio Palumbo. A functional data analysis approach for the monitoring of ship CO2 emissions. Gestão & Produção, 28(3), 2021
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