研究人员正在探索深度学习模型在各个领域的预测性维护和性能分析。一项研究利用CNN和LSTM网络，结合德克萨斯州的大量路面状况数据来模拟劣化，结果显示CNN优于标准的机器学习。另一篇论文侧重于通过强调数据预处理再应用时间卷积网络（TCN）来改进航空发动机的剩余使用寿命（RUL）预测，在NASA C-MAPSS数据集上展示了卓越的准确性。此外，对基于PPG的情感识别的深度学习架构（CNN、Transformer、Mamba）的比较表明，CNN对于可穿戴监测系统仍然有效。 AI
影响 展示了深度学习在预测性维护和信号分析应用方面的进展，有望提高基础设施管理和机械预测的效率。
排序理由 多篇arXiv论文详细介绍了新颖的深度学习应用和比较。
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arXiv:2605.01914v1 Announce Type: new Abstract: The deterioration of pavement is a complex and dynamic process determined by different factors including material, environment, design, and some other unobserved variables. Accurate predictions of pavement condition can help maximiz…
arXiv:2605.02507v1 Announce Type: new Abstract: Accurate prediction of Remaining Useful Life (RUL) in aero-engines is vital for predictive maintenance, improved operational reliability, and reduced lifecycle costs. While deep learning approaches have demonstrated strong potential…
Accurate prediction of Remaining Useful Life (RUL) in aero-engines is vital for predictive maintenance, improved operational reliability, and reduced lifecycle costs. While deep learning approaches have demonstrated strong potential in this area, most existing methods focus prima…
arXiv:2604.26078v1 Announce Type: new Abstract: Photoplethysmography (PPG) is increasingly used in wearable affective computing due to its low cost and ease of integration into consumer devices. Recent advances in deep learning have introduced long-range sequence models, such as …