Improved Ensemble Learning for Wind Turbine Main Bearing Fault Diagnosis

Autor: Mattia Beretta, Yolanda Vidal, Jose Sepulveda, Olga Porro, Jordi Cusidó

Publikováno v: Applied Sciences, Vol 11, Iss 16, p 7523 (2021)

The goal of this paper is to develop, implement, and validate a methodology for wind turbines’ main bearing fault prediction based on an ensemble of an artificial neural network (normality model designed at turbine level) and an isolation forest (a

Externí odkaz: https://doaj.org/article/1dc94ad0774b463f9d0077cd6e6d5923

Improved Ensemble Learning for Wind Turbine Main Bearing Fault Diagnosis

Autor: Olga Porro, Jose Sepulveda, Mattia Beretta, Jordi Cusidó, Yolanda Vidal

Publikováno v: Applied Sciences, Vol 11, Iss 7523, p 7523 (2021)
Applied Sciences
Volume 11
Issue 16
UPCommons. Portal del coneixement obert de la UPC
Universitat Politècnica de Catalunya (UPC)

The goal of this paper is to develop, implement, and validate a methodology for wind turbines’ main bearing fault prediction based on an ensemble of an artificial neural network (normality model designed at turbine level) and an isolation forest (a

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e969436fa75fa9d1c2894cc68d5ce240
https://www.mdpi.com/2076-3417/11/16/7523

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