随着自动故障检测和诊断工具准备解决建筑能源系统良好调试和维护实践的许多障碍，人工智能和机器学习领域的技术正在成为可行的方法，而基于规则的技术可能不太合适。本文描述了一种新的基于机器学习的动态故障检测技术。基于从大型建筑实验室设备获得的真实故障数据，初步结果显示了该技术的性能，并对当前研究和未来研究方向进行了讨论。引用:ASHRAE会议论文，蒙特利尔，QC

With automated fault detection and diagnostics tools poised to address many of the barriers to good commissioning and maintenance practice in building energy systems, techniques from the artificial intelligence and machine learning domains are emerging as viable approaches where rules-based techniques can be less suitable. This paper describes a novel dynamic, machine learning-based technique for detecting faults in commercial air handling units. Preliminary results showing the performance of the proposed technique based on real world fault data obtained from a large scale building laboratory facility are presented, with discussion on current research and future research direction also provided.