Wang Seunghyeon
Institute for Environmental Design and Engineering, University College London, London, WC1H 0NN, UK.
Sci Data. 2025 Aug 25;12(1):1481. doi: 10.1038/s41597-025-05825-9.
This study aims to develop comprehensive real operational datasets from three distinct building types-a large-scale office, an auditorium, and a hospital-focusing on Air Handling Units (AHUs) equipped with Constant Air Volume (CAV) systems for Automated Fault Detection and Diagnosis (AFDD). Although a consistent methodological framework was followed, data collection and preparation processes were specifically adapted to each building's unique operational characteristics. Key procedures included: (1) customized raw data collection based on individual building requirements; (2) thorough identification and removal of missing or duplicated data points; (3) systematic annotation of operational conditions and fault categories; and (4) strategic division of datasets into training, validation, and test subsets tailored to each building's specific data features. The resulting datasets enable researchers and developers to refine and advance machine learning and diagnostic models specifically designed for AFDD within AHU systems. Facility operators can then seamlessly integrate these validated AFDD models into existing management systems, facilitating efficient automated fault detection and ensuring optimal performance and reliability.
本研究旨在从三种不同类型的建筑——大型办公室、礼堂和医院——开发全面的实际运行数据集，重点关注配备定风量（CAV）系统的空气处理机组（AHU），用于自动故障检测与诊断（AFDD）。尽管遵循了一致的方法框架，但数据收集和准备过程根据每栋建筑独特的运行特性进行了专门调整。关键步骤包括：（1）根据各建筑需求定制原始数据收集；（2）彻底识别和清除缺失或重复的数据点；（3）对运行条件和故障类别进行系统标注；（4）根据每栋建筑的特定数据特征，将数据集战略性地划分为训练、验证和测试子集。所得数据集使研究人员和开发人员能够完善和推进专门为AHU系统中的AFDD设计的机器学习和诊断模型。设施运营商随后可将这些经过验证的AFDD模型无缝集成到现有管理系统中，促进高效的自动故障检测，并确保最佳性能和可靠性。
