在光纤传感器中的人工智能和机器学习:一篇评论
Lidan Cao1, Sabrina Abedin1, Guoqiang Cui1
1Electrical and Computer Engineering Department, University of Massachusetts Lowell, Lowell, MA 01854, USA.
Sensors (Basel, Switzerland)
|December 31, 2025
概括
人工智能 (AI) 增强光纤传感 (OFS) 以实现更智能的系统. 本综述涵盖了本地化和分布式OFS中的AI算法和应用,以提高性能和数据解释.
科学领域:
- 光子学和传感器技术技术
- 人工智能和机器学习
背景情况:
- 光纤传感 (OFS) 提供了强大的和多功能传感能力.
- 与人工智能 (AI) 的整合有望显著提高OFS的性能和适应性.
- 现有的OFS技术,包括本地化 (FBG,FP,MZI) 和分布式 (Rayleigh,Brillouin,Raman) 系统,可以从人工智能驱动的进步中受益.
研究的目的:
- 提供对人工智能增强的光纤传感 (OFS) 技术的全面审查.
- 探索适用于OFS的各种AI算法.
- 突出AI在优化OFS设计,操作和数据分析方面的变革性应用.
主要方法:
- 对局部化OFS传感器的审查:纤维布拉格格 (FBG),法布里-佩罗 (FP) 干扰仪,马赫-泽恩德干扰仪 (MZI).
- 分布式OFS系统的审查:基于雷利,布里卢恩和拉曼散射的方法.
- 讨论人工智能算法:监督学习,无监督学习,强化学习和深度神经网络.
主要成果:
- 人工智能算法被有效地应用于增强传感器设计和优化审讯系统.
- 人工智能可以实现自适应配置调整和复杂传感器输出的高级解释.
- 关键应用包括OFS数据中的无声化,分类,事件检测和故障预测.
结论:
- 人工智能和OFS之间的协同作用正在创造更智能,更适应,更高性能的传感解决方案.
- 人工智能集成显著提高了本地化和分布式光纤传感的能力.
- 未来的进步在于利用人工智能在OFS中进行更复杂的数据解释和预测性维护.
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