SSAE-MLP: Stacked sparse autoencoders-basedmulti-layer perceptron for main bearing temperature prediction of large-scale wind turbines

摘要

Condition monitoring and fault diagnosis of main bearings of large-scale wind turbines is critical for improving its reliability and reducing operating and maintenance costs, especially in the early stages. To achieve the goal, this paper proposes a novel deep learning approach named stacked sparse autoencoder multi-layer perceptron (SSAE-MLP) with a new framework by utilizing supervisory control and data acquisition (SCADA) data for wind turbine main bearing temperature prediction. After the SCADA parameter variables related to the temperature change of the main bearing are extracted, the input characteristic vector is constructed. Then, the multiple sparse autoencoders are stacked to learn the deep features inside the input data by applying the greedy layerwise unsupervised learning algorithm. Finally, a regression predictor is added to the top layer of the stacked sparse autoencoder model for supervised learning to fine-tune the overall network. Comparative experiments show that the proposed approach has superior performance for wind turbine main bearing temperature prediction.