An Adaptive Approach for Ice Detection in Wind Turbine With Inductive Transfer Learning

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Ice-accretion on blades of a wind turbine will cause power degradation and threaten the operating safety of the unit. The use of a machine learning method for ice detection is a promising solution. However, it is costly and infeasible to establish a well-trained model for each individual unit. This paper proposes an inductive transfer learning method to address this problem. The inductive transfer learning aims to improve the detection performance by transferring knowledge from a well-established model. As there is a distribution divergence between the source and target domain, most instance-transfer-based learning methods realize the knowledge transfer by re-weighting the instance in the source domain. In this paper, we propose a novel adaptive inductive transfer learning (adaptive ITL) method for wind turbine ice detection. The knowledge transfer is achieved by offering a relatively stable prediction for the target task through the established model trained in source task. Two auxiliary classifiers are then employed to correct the prediction error of the previous prediction. The model in the source domain and two auxiliary classifiers are combined into a whole model to make a further prediction for the target task. The experimental results on ice detection in wind turbine demonstrate that the proposed adaptive ITL method can significantly improve the performance of the basic instance-transfer-based model and is superior to the state-of-the-art inductive transfer learning methods.