A nearly end-to-end deep learning approach to fault diagnosis of wind turbine gearboxes under nonstationary conditions.
Liangwei ZhangQi FanJing LinZhicong ZhangXiaohui YanChuan LiPublished in: Eng. Appl. Artif. Intell. (2023)
Keyphrases
- end to end
- non stationary
- fault diagnosis
- deep learning
- wind turbine
- operating conditions
- wind speed
- neural network
- unsupervised learning
- expert systems
- wind power
- fault detection
- machine learning
- fuzzy logic
- power generation
- random fields
- rbf neural network
- power plant
- autoregressive
- concept drift
- weakly supervised
- mental models
- fault identification
- bp neural network
- training data
- multiscale
- feature selection