用转移学习用于异常检测的哈德龙热量计数据质量监测
Mulugeta Weldezgina Asres1, Christian Walter Omlin1, Long Wang2
1Centre for Artificial Intelligence Research, Department of Information and Communication Technology, University of Agder, 4879 Grimstad, Norway.
Sensors (Basel, Switzerland)
|September 19, 2025
概括
转移学习显示了复杂的时空异常检测的前景. 这种方法有效地利用预先训练的模型来提高准确性,并减少对许多传感器系统的数据需求.
科学领域:
- 数据科学数据科学数据科学
- 机器学习 机器学习
- 物理 仪器仪表 物理仪器仪表
背景情况:
- 传感器的扩散产生了大量的时空 (ST) 数据,使数据策划和分析部署复杂化.
- 转移学习 (TL) 可以通过利用预先训练的模型来解决数据稀疏性和模型复杂性.
- 关于将TL应用于复杂的ST模型以检测异常 (AD) 的研究有限.
研究的目的:
- 研究TL的潜力和局限性,用于高维STAD.
- 提高模型的准确性和稳定性,尤其是在有限的训练数据的情况下.
- 探索TL在数千个传感器的复杂系统中的有效性.
主要方法:
- 开发了一个混合自编码器架构,结合了卷积,图形和循环神经网络.
- 在CERN的哈德龙热量计的不同部分训练的模型上应用了TL.
- 分析了编码器和解码器网络的可转移性.
主要成果:
- 证明了TL在减少可训练参数的情况下提高ST AD性能的能力.
- 提供了对有效模型初始化和训练配置的见解.
- 通过TL展示了通过TL减轻数据污染效应.
结论:
- 在复杂的STAD任务中,TL是一个可行的策略,特别是在数据稀缺的环境中.
- 混合自编码器架构与TL提供了更高的准确性和稳定性.
- 进一步的研究可以优化TL用于基于传感器的异常检测系统.
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