Farina Dario, Machrafi Hatim, Queeckers Patrick, Dongo Patrice D, Iorio Carlo Saverio
Centre for Research and Engineering in Space Technologies (CREST), Department of Aero-Thermo-Mechanics, Université Libre de Bruxelles, 1050 Bruxelles, Belgium.
GIGA-In Silico Medicine, Université de Liège, 4000 Liège, Belgium.
Nanomaterials (Basel). 2024 Jul 1;14(13):1135. doi: 10.3390/nano14131135.
Ice formation on aircraft surfaces poses significant safety risks, and current detection systems often struggle to provide accurate, real-time predictions. This paper presents the development and comprehensive evaluation of a smart ice control system using a suite of machine learning models. The system utilizes various sensors to detect temperature anomalies and signal potential ice formation. We trained and tested supervised learning models (Logistic Regression, Support Vector Machine, and Random Forest), unsupervised learning models (K-Means Clustering), and neural networks (Multilayer Perceptron) to predict and identify ice formation patterns. The experimental results demonstrate that our smart system, driven by machine learning, accurately predicts ice formation in real time, optimizes deicing processes, and enhances safety while reducing power consumption. This solution holds the potential for improving ice detection accuracy in aviation and other critical industries requiring robust predictive maintenance.
飞机表面结冰会带来重大安全风险，而当前的检测系统往往难以提供准确的实时预测。本文介绍了一种使用一系列机器学习模型的智能冰控系统的开发与综合评估。该系统利用各种传感器检测温度异常并发出潜在结冰信号。我们训练并测试了监督学习模型（逻辑回归、支持向量机和随机森林）、无监督学习模型（K均值聚类）以及神经网络（多层感知器）来预测和识别结冰模式。实验结果表明，我们的智能系统由机器学习驱动，能够实时准确预测结冰情况，优化除冰过程，提高安全性并降低功耗。该解决方案有望提高航空及其他需要强大预测性维护的关键行业的结冰检测精度。
