Designing health indicators (HIs) for aerospace composite structures that demonstrate their health comprehensively, including all types of damage that can be adaptively updated, is challenging, especially under complex conditions like impact and compression-fatigue loadings. This paper introduces a new AI-based approach to designing reliable HIs (fulfilling requirements—monotonicity, prognosability, and trendability—referred to as ’Fitness’) for single-stiffener composite panels under fatigue loading using acoustic emission sensors. It incorporates complete ensemble empirical mode decomposition with adaptive noise for feature extraction, semi-supervised base deep learner models made of long short-term memory layers for information fusion, and a semi-supervised paradigm to simulate labels inspired by the physics of progressive damage. In this way, nondifferentiable prognostic criteria are implicitly implemented into the learning process. Ensemble learning, especially using a semi-supervised network built with bidirectional long short-term memory, improves HI quality while reducing deep learning randomness. The Fitness function equation has been modified to provide a more trustworthy foundation for comparison and enhance the practical reliability of the standard in prognostics and health management. Ablation experiments are conducted, including variations in dataset division and leave-one-out cross-validation, confirming the generalizability of the approach.

中文翻译：

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一种使用声波的新型智能健康指标：CEEMDAN 驱动的半监督集成深度学习


为航空航天复合材料结构设计健康指标 （HI） 以全面证明其健康状况，包括可以自适应更新的所有类型的损伤，这非常具有挑战性，尤其是在冲击和压缩疲劳载荷等复杂条件下。本文介绍了一种基于 AI 的新方法，用于使用声发射传感器为疲劳载荷下的单加筋复合板设计可靠的 HI（满足要求——单调性、预测性和趋势性——称为“适应性”）。它结合了完整的集成经验模态分解和自适应噪声，用于特征提取，由长短期记忆层组成的半监督基础深度学习模型用于信息融合，以及用于模拟受渐进损伤物理学启发的标签的半监督范式。通过这种方式，不可微分的预后标准被隐式地实施到学习过程中。集成学习，特别是使用由双向长短期记忆构建的半监督网络，可以提高 HI 质量，同时减少深度学习随机性。对 Fitness 函数方程进行了修改，为比较提供了更值得信赖的基础，并增强了该标准在预后和健康管理方面的实际可靠性。进行了消融实验，包括数据集划分和留一法交叉验证的变化，证实了该方法的通用性。