Abstract
Facing supercooled large water droplet environment, an effective ice detection method is a prerequisite to implement the avoidance strategy and get out of the icing environment of SLD as soon as possible. Fiber-optic icing sensors were arranged on the double impact surface probe. The probe was used for icing wind tunnel test. Different machine learning algorithms were used to establish the classification method of icing conditions based on multi-sensor ice thickness information fusion. An appropriate algorithm was selected for the classification method to detect icing conditions. The icing classification method based on SVM could effectively distinguish the conventional water droplet icing condition from the SLD icing condition, and it has significant potential on aviation industry application.
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