As vital renewable energy devices, wind turbines suffer from gearbox failures due to harsh speed increasing operations. Therefore, gearbox fault diagnosis is crucial for wind turbine maintenance with reducing economic costs. However, obtaining faulty data is rather challenging, especially at the early fault stage. For this reason, a sparse isolation encoding forest (SIEF) is proposed aiming at both anomaly detection and novel fault discrimination for wind turbine gearboxes. In the present SIEF method, a sparse autoencoder (SAE) is first trained with only normal data to obtain an optimized and robust weight structure. Newly acquired data corresponding to faulty or healthy conditions are sent to this encoder for feature extraction by encoding to its low-dimensional space. All the data in low-dimensional space are fed to an isolation forest (IF) for anomaly detection and novel fault discrimination. In the addressed SIEF approach, only normal data are required to train the model for fault detection and further discrimination. It is consistent with the actual operations of the wind turbines, with much less dependence on the fault data for model training. The proposed method was evaluated by fault diagnosis tests on wind turbine gearboxes. Results show good performances of the proposal compared with peers.

The gearbox, as a key transmission device in the industrial field, may lead to severe vibrations or even failures when abnormalities occur. Therefore, with the increasing complexity of industrial automation, precise anomaly localization has become crucial. To address this issue, a gearbox condition monitoring method based on an unsupervised deep convolutional support generative adversarial network (DCSGAN) is proposed. First, high-dimensional data collected is used to train the generator, and samples are generated by the generator to calculate their reconstruction errors. Next, these reconstruction errors are utilized to train one-class support vector machine (OCSVM). During the testing phase, reconstruction errors are similarly calculated for the test data, and after being normalized using the same process as the training data, the errors are input into the trained OCSVM model for anomaly detection. The proposed method has been validated on a real gearbox dataset, and experimental results indicate that the DCSGAN outperforms other models in anomaly detection. The GitHub code for the proposed DCSGAN has been made public at: https://github.com/MR-ach/DCSGAN.

Due to harsh and variable working conditions, the wind turbine gearbox may be damaged during the operation, resulting in an extended downtime with reduced productivity and economic loss. This calls for efficient fault diagnostics for the wind turbine gearboxes. Commonly-used diagnostics based on classical deep learning networks cannot guarantee good performance with time series signals due to the weakness of feature extraction. For this reason, an efficient channel attention residual learning approach is proposed to enhance the feature extraction and fault diagnosis for wind turbine gearboxes, leading to the development of a channel attention residual network (CAResNet). The collected time series signals are directly employed as the input. The efficient channel attention is embedded into the residual network to capture features and improve fault diagnosis capability. Experiments are carried out on a real wind turbine gearbox. The results showed that the average diagnostic accuracy of the CAResNet model was 94.41%. CAResNet has better diagnostic accuracy than other methods proposed in this paper.