在故障和无故障状态中运行的空气处理单元的标记数据集.
Naghmeh Ghalamsiah1, Jin Wen2, Guowen Li3
1Department of Civil, Architectural, and Environmental Engineering, Drexel University, Philadelphia, PA, USA.
Scientific data
|January 9, 2026
概括
本研究引入了八个新的数据集,用于空气处理装置 (AHU) 故障检测和诊断. 它还确定了最佳的AHU数据集对,用于评估HVAC系统中的转移学习 (TL) 算法.
科学领域:
- 建筑能源系统 建筑能源系统
- 工程领域的人工智能
- 数据科学用于基础设施的数据科学.
背景情况:
- 对HVAC系统的数据驱动故障检测和诊断 (FDD) 正在取得进展.
- 对FDD进行标记的数据集很少,这阻碍了新方法的开发.
- 转移学习 (TL) 对不同建筑物的FDD显示出希望,但需要合适的比较数据集.
研究的目的:
- 为了解决缺乏对建筑系统TL评估的比较研究的问题.
- 发布空气处理装置 (AHU) 故障分析的新数据集.
- 为了确定最佳的AHU数据集对来评估TL算法.
主要方法:
- 发布了八个新的AHU数据集,涵盖无故障和各种故障条件.
- 对AHU故障数据集的全面分析,以确定TL的合适对.
- 评估数据集的相似性和差异,用于跨数据集的转移学习.
主要成果:
- 现在有八个新的AHU数据集公开提供.
- 确定适用于TL评估的特定AHU故障数据集对.
- 建立了用于在构建FDD时选择比较数据集的方法.
结论:
- 新的数据集和比较分析促进了用于建筑HVAC FDD的TL的研究.
- 这项工作使得在各种建筑系统中对TL算法的更强大的评估成为可能.
- 为建立FDD加快了数据集间研究的进展.
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