Ice accumulations on structures and rotating blades could introduce significant issues resulting in structure failure, fatigue load increments, safety hazards, etc. Ice detection and anti−/de-icing systems for rotorcraft, aircraft, or wind turbines operating in cold climates become important. This paper introduces a novel ice detection method based on an artificial intelligent technique. The main idea for the proposed ice detection system is that the accumulated ice positions and masses are predicted by the modal property changes, i.e., the natural frequencies. To this end, a deep neural network (DNN) is applied to detect ice mass distributions by considering the variations of the natural frequencies of the slender and flexible rotating and non-rotating blades. To design a refined DNN model, hyperparameter optimization is applied. Furthermore, a transfer learning method is adapted to extend the trained DNN model for the non-rotating blade to the rotating blade. As a result, the parameters related to DNN model are intensively analyzed to design the optimized network. Overall, the proposed method to construct an optimum DNN model as the ice detection system successfully predicts ice mass distributions. In addition, the established DNN model can be easily extended to the new icing scenarios.

结构和旋转叶片上的积冰可能会导致结构失效、疲劳载荷增加、安全隐患等重大问题。在寒冷气候下运行的旋翼飞机、飞机或风力涡轮机的冰检测和防冰/除冰系统变得很重要。本文介绍了一种基于人工智能技术的新型冰检测方法。所提出的冰检测系统的主要思想是通过模态属性变化（即固有频率）来预测积冰的位置和质量。为此，应用深度神经网络（DNN）通过考虑细长和柔性旋转和非旋转叶片的固有频率的变化来检测冰块分布。为了设计一个细化的 DNN 模型，应用了超参数优化。此外，迁移学习方法适用于将训练好的非旋转叶片 DNN 模型扩展到旋转叶片。因此，对与 DNN 模型相关的参数进行了深入分析，以设计优化的网络。总体而言，所提出的构建最佳 DNN 模型的方法作为冰检测系统成功地预测了冰块分布。此外，建立的 DNN 模型可以很容易地扩展到新的结冰场景。