@inproceedings{da7b967b4c204382808a1745727bc942,
title = "Severity estimation of stator winding short-circuit faults using cubist",
abstract = "In this paper, an approach to estimate the severity of stator winding short-circuit faults in squirrel-cage induction motors based on the Cubist model is proposed. This is accomplished by scoring the unbalance in the current and voltage waveforms as well as in Park{\textquoteright}s Vector, both for current and voltage. The proposed method presents a systematic comparison between models, as well as an analysis regarding hyper-parameter tunning, where the novelty of the presented work is mainly associated with the application of data-based analysis techniques to estimate the stator winding short-circuit severity in three-phase squirrel-cage induction motors. The developed solution may be used for tele-monitoring of the motor condition and to implement advanced predictive maintenance strategies.",
keywords = "Cubist, Fault diagnosis, Induction motor, Inter-turn short-circuit, Machine learning, Regression, Severity estimation",
author = "{dos Santos}, Tiago and Ferreira, {Fernando J.T.E.} and Pires, {Jo{\~a}o Moura} and Dam{\'a}sio, {Carlos Viegas}",
note = "sem pdf. FCT - Funda{\c c}{\~a}o para a Ci{\^e}ncia e Tecnologia MCTES, UID/CEC/04516/2013 (NOVA LINCS).; 18th EPIA Conference on Artificial Intelligence, EPIA 2017 ; Conference date: 05-09-2017 Through 08-09-2017",
year = "2017",
month = jan,
day = "1",
doi = "10.1007/978-3-319-65340-2_18",
language = "English",
isbn = "978-3-319-65339-6",
volume = "10423 LNAI",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "217--228",
booktitle = "Progress in Artificial Intelligence - 18th EPIA Conference on Artificial Intelligence, EPIA 2017, Proceedings",
address = "Germany",
}