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Wind turbines drive train fault detection: random forests vs CNNs
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Orientador(es)
Resumo(s)
The production of wind-powered energy is harvested by huge wind turbines installed in locations where winds are strong but difficult to access. Detecting minor severity faults allows for the scheduling of defective component replacement during planned maintenance dates, before the fault severity increases. This significantly reduces maintenance costs.
A key component of wind turbines is the drivetrain, which transfers mechanical energy from the rotating blades to an electric energy generator.
This gearbox system is quite exposed to faults, such as damaged gears and broken or worn teeth.
This paper presents and discuss the identification and classification of gearbox faults using vibration and acoustic emission signals. It is shown that Random Forests (RFs) classifiers can be trained to achieve 100% accuracy rate, by performing previously classical feature extraction[8] on the raw signals, while Convolutional Neural Networks (CNNs) classifiers
also achieve 100% accuracy rates, directly on raw signals and with a shorter duration than required by RF classifiers.
Descrição
Palavras-chave
Fault detection Wind turbine Drivetrain Machine learning Deep learning
Contexto Educativo
Citação
Editora
Springer
Licença CC
Sem licença CC