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• W4289209204 abstract "Wind turbines require suitable maintenance management operations. Supervisory control and data acquisition system is widely applied in the industry since large volumes of data are obtained, being necessary the application of advanced analysis techniques. The analysis of the alarms is a critical phase in the industry. This work proposes a novel approach to analyse the alarm activations. It combined statistical methods and deep learning algorithms to increase the reliability of the analysis. Initial data filtering, principal component analysis and correlations are applied to increase the reliability of the neural network. It proposed a case study formed by data from a real wind turbine, and the results demonstrate an increment in the reliability of the artificial neural network." @default.
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• W4289209204 date "2022-01-01" @default.
• W4289209204 modified "2023-09-25" @default.
• W4289209204 title "Wind Turbine Alarm Management with Artificial Neural Networks" @default.
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• W4289209204 doi "https://doi.org/10.1007/978-981-19-1012-8_1" @default.
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