Developing comprehensive health indicators (HIs) for composite structures encompassing various damage types is challenging due to the stochastic nature of damage accumulation and uncertain events (like impact) during operation. This complexity is amplified when striving for HIs independent of historical data. This paper introduces an AI-driven approach, the Hilbert transform-convolutional neural network under a semi-supervised learning paradigm, to designing reliable HIs (fulfilling requirements, referred to as 'fitness'). It exclusively utilizes current guided wave data, eliminating the need for historical information. Ensemble learning techniques were also used to enhance HI quality while reducing deep learning randomness. The fitness equation is refined for dependable comparisons and practicality. The methodology is validated through investigations on T-single stiffener CFRP panels under compression-fatigue and dogbone CFRP specimens under tension-fatigue loadings, showing high performance of up to 93% and 81%, respectively, in prognostic criteria.

中文翻译：

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一种新颖的机器学习模型，用于设计复合结构的历史独立健康指标

由于运行过程中损伤累积和不确定事件（如冲击）的随机性，为包含各种损伤类型的复合结构开发综合健康指标（HI）具有挑战性。当追求独立于历史数据的 HI 时，这种复杂性会被放大。本文介绍了一种人工智能驱动的方法，即半监督学习范式下的希尔伯特变换卷积神经网络，用于设计可靠的 HI（满足要求，称为“适应度”）。它专门利用当前的导波数据，无需历史信息。集成学习技术也被用来提高 HI 质量，同时减少深度学习的随机性。健身方程经过改进，可进行可靠的比较和实用性。该方法通过对压缩疲劳载荷下的 T 形单加强筋 CFRP 面板和拉伸疲劳载荷下的狗骨 CFRP 样本的研究进行了验证，在预测标准中显示出分别高达 93% 和 81% 的高性能。