使用基于预训练过的变压器的转移学习方法检测网络物理系统的故障
Pooya Sajjadi1, Fateme Dinmohammadi1, Mahmood Shafiee2
1School of Computing and Engineering, University of West London, London W5 5RF, UK.
Sensors (Basel, Switzerland)
|July 12, 2025
概括
使用变压器进行转移学习通过利用预训练模型来增强网络物理系统 (CPS) 的故障检测. 这种方法克服了数据的局限性,改善了工业应用的预测和健康管理 (PHM).
科学领域:
- 工程 工程师 工程师 工程师
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 网络物理系统 (CPS) 对现代工业至关重要,但故障可能会造成重大破坏.
- 预后和健康管理 (PHM) 对于CPS可靠性至关重要.
- 深度学习 (DL) 方法在CPS应用中面临着不平衡和稀缺的故障标记数据的挑战.
研究的目的:
- 建议使用预训练过的变压器进行转移学习方法,以改善CPS中的故障检测.
- 为应对实验室和现实世界工业数据之间的领域转移挑战.
- 加强DL在工业CPS设置中的部署,故障数据有限.
主要方法:
- 在大型源数据集上预训练简化的变压器模型.
- 微调模型端到端在一个较小的目标数据集上,具有不同的分布.
- 应用SHAP (夏普利添加式解释) 对可解释AI (XAI) 来理解模型决策.
主要成果:
- 拟议的转移学习模型在净水设施数据集上获得了93.38%的F1平均得分.
- 基于变压器的方法表现优于基线模型,如卷积神经网络 (CNN) 和长短期记忆 (LSTM).
- 在具有有限故障数据的工业CPS设置中证明了实际有效性.
结论:
- 基于变压器的转移学习是增强工业CPS故障检测的可行解决方案.
- 该方法有效地转移诊断知识,缓解数据稀缺和域转移的问题.
- 像SHAP这样的可解释AI (XAI) 方法可以提高故障检测模型的透明度.
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