A Model-Agnostic Meta-Baseline Method for Few-Shot Fault Diagnosis of Wind Turbines

The technology of fault diagnosis is helpful to improve the reliability of wind turbines, and further reduce the operation and maintenance cost at wind farms. However, in reality, wind turbines are not allowed to operate with faults, so few fault samples could be obtained. With a small amount of training data, traditional fault diagnosis models that need huge samples under a deep learning framework are difficult to maintain with high accuracy and effectiveness. Few-shot learning can effectively solve the problem of overfitting caused by fewer fault samples in model training. In view of model-agnostic meta-learning (MAML), this paper proposes a model for few-shot fault diagnosis of the wind turbines drivetrain, which is named model-agnostic meta-baseline (MAMB). The training data is input to the base classification model for pre-training, then, some data is randomly selected from the training set to form multiple meta-learning tasks that are utilized to train the MAML to finally fine-tune the later layers of the model at a smaller learning rate. The proposed model was analyzed by the small samples of the bearing data from Case Western Reserve University (CWRU) data, the generator bearings, and gearboxes vibration data in wind turbines under randomly changing operating conditions. The results verified that the proposed method was superior in one-shot, five-shot, and ten-shot tasks of wind turbines.