2026-04-21T05:13:57Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/99832025-01-22T01:05:31Zcom_10259_4219com_10259_5086com_10259_2604col_10259_4220

Maestro Prieto, José Alberto Ramírez Sanz, José Miguel Bustillo Iglesias, Andrés Rodríguez Diez, Juan José 2025-01-21T13:56:48Z 2025-01-21T13:56:48Z 2024 0924-669X http://hdl.handle.net/10259/9983 10.1007/s10489-024-05373-6 1573-7497 Both wear-induced bearing failure and misalignment of the powertrain between the rotor and the electrical generator are common failure modes in wind-turbine motors. In this study, Semi-Supervised Learning (SSL) is applied to a fault detection and diagnosis solution. Firstly, a dataset is generated containing both normal operating patterns and seven different failure classes of the two aforementioned failure modes that vary in intensity. Several datasets are then generated, maintaining different numbers of labeled instances and unlabeling the others, in order to evaluate the number of labeled instances needed for the desired accuracy level. Subsequently, different types of SSL algorithms and combinations of algorithms are trained and then evaluated with the test data. The results showed that an SSL approach could improve the accuracy of trained classifiers when a small number of labeled instances were used together with many unlabeled instances to train a Co-Training algorithm or combinations of such algorithms. When a few labeled instances (fewer than 10% or 327 instances, in this case) were used together with unlabeled instances, the SSL algorithms outperformed the result obtained with the Supervised Learning (SL) techniques used as a benchmark. When the number of labeled instances was sufficient, the SL algorithm (using only labeled instances) performed better than the SSL algorithms (accuracy levels of 87.04% vs. 86.45%, when labeling 10% of instances). A competitive accuracy of 97.73% was achieved with the SL algorithm processing a subset of 40% of the labeled instances. Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. application/pdf eng Springer Applied Intelligence. 2024, V. 54, n. 6, p. 4525-4544 https://doi.org/10.1007/s10489-024-05373-6 Atribución 4.0 Internacional http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess Wind turbine Powertrain failures Bearing failures Semi-supervised learning Fault detection and diagnosis Informática Bioinformática Computer science Bioinformatics Semi-supervised diagnosis of wind-turbine gearbox misalignment and imbalance faults info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Applied Intelligence 54 6 4525 4544