检讨故障诊断方法:从传统的机器学习到大型语言模型融合范式
Qingwei Nie1, Junsai Geng1, Changchun Liu2
1School of Mechanical Engineering, Yangzhou University, Yangzhou 225000, China.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
智能故障诊断从信号分析演变为人工智能. 最近的进展整合了大型语言模型,数字双胞胎和知识图,以提高工业系统的安全性和效率.
科学领域:
- 工业工程 工业工程 工业工程
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 故障诊断对于工业系统的安全性和效率至关重要.
- 传统的信号分析正在被智能,算法驱动的方法所取代.
- 最近大型语言模型 (LLM),数字双胞胎和知识图的整合标志着智能整合的新阶段.
研究的目的:
- 系统地审查故障诊断技术的演变.
- 分析传统机器学习,数字双胞胎,知识图和法学士的理论方法和实际应用.
- 为研究人员和从业人员提供全面的技术地图和参考资料.
主要方法:
- 传统机器学习 (监督和无监督学习) 的审查.
- 检查数字双胞胎 (物理虚拟映射),知识图 (知识建模) 和LLM (特征学习) 的创新应用.
- 构建一个多维的比较框架,以基于性能,场景和协作潜力来评估技术.
主要成果:
- 分析各种故障诊断方法的原则,应用和局限性.
- 性能指标的比较,适用的场景,以及不同技术的协作潜力.
- 确定关键挑战,包括数据质量,模型概括和知识再利用.
结论:
- 线程管理,数字双胞胎和知识图的融合代表了智能故障诊断的未来方向.
- 该研究建立了一个技术地图,以支持理论创新和工程部署.
- 该审查为推进工业系统智能故障诊断提供了最新的参考.
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