Fault detection, diagnostics, and prognostics (FDD&P) ensure the operation efficiency and safety of engineering systems. In the building domain, they can help significantly reduce energy consumption and improve occupant comfort. Specifically, prognostics are becoming increasingly important as a pro-active fault prevention strategy through continuously monitoring the health of energy systems. In this article, we develop a machine learning-based method for building systems. The proposed method can help develop predictive models from historical operation and maintenance data. After the detailed description of the proposed machine learning-based prognostic method, a case study involving prognostics on central heating and cooling plant (CHCP) equipment is provided. To this end, a year’s worth of sensor and actuator data from four boilers and five chillers of a CHCP in Ottawa, Canada are collected. The plant operators are interviewed to understand how they handle failure events, and their logbooks are reviewed to extract the date and time of the recorded failure events. The sensor and actuator data up to two weeks prior to each of these failure events are used to develop regression tree models that predict time to failure (TTF). The results indicate that about half of the modeled failure events could be accurately predicted by looking at the data available in the distributed control system. Finally, the future work is outlined. Note to Practitioners —This article was motivated by the problem of fault detection, diagnostics, and prognostics (FDD&P) of the building systems. We contemplated to develop an advanced technology for heating, ventilation and air conditioning (HVAC) prognostics, in particular, for central heating and cooling plant (CHCP) health monitoring, aiming to save energy consumption and the operational cost. The developed machine learning-enabled predictive modeling technique, which can help build predictive models from historic operational and maintenance data, can be applied to other application domains such as oil pipeline system monitoring and high-speed train prognostics.

中文翻译：

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基于机器学习的集中供热和制冷设备设备健康监测的预测

故障检测，诊断和预测（FDD＆P）确保工程系统的运行效率和安全性。在建筑领域，它们可以帮助显着降低能耗并改善乘员舒适度。具体而言，通过主动监视能源系统的健康状况，作为主动故障预防策略，预测的重要性日益提高。在本文中，我们开发了一种基于机器学习的构建系统方法。所提出的方法可以帮助根据历史运行和维护数据开发预测模型。在对所提出的基于机器学习的预测方法进行详细描述之后，将提供一个涉及中央供热和制冷设备（CHCP）设备预测的案例研究。为此，从加拿大渥太华的一个CHCP的四个锅炉和五个冷却器收集了一年的传感器和执行器数据。采访了工厂操作员以了解他们如何处理故障事件，并审查了他们的日志以提取记录的故障事件的日期和时间。在每个故障事件发生之前的两周之前，传感器和执行器的数据将用于开发预测故障时间（TTF）的回归树模型。结果表明，通过查看分布式控制系统中可用的数据，可以准确预测大约一半的建模故障事件。最后，概述了未来的工作。审查其日志，以提取记录的故障事件的日期和时间。在每个故障事件发生之前的两周之前，传感器和执行器的数据将用于开发预测故障时间（TTF）的回归树模型。结果表明，通过查看分布式控制系统中可用的数据，可以准确预测大约一半的建模故障事件。最后，概述了未来的工作。审查其日志，以提取记录的故障事件的日期和时间。在每个故障事件发生之前的两周之前，传感器和执行器的数据将用于开发预测故障时间（TTF）的回归树模型。结果表明，通过查看分布式控制系统中可用的数据，可以准确预测大约一半的建模故障事件。最后，概述了未来的工作。执业者注意 —本文受到建筑系统故障检测，诊断和预测（FDD＆P）问题的启发。我们计划开发一种用于供暖，通风和空调（HVAC）预测的先进技术，尤其是用于中央供暖和制冷设备（CHCP）健康监测的技术，以节省能源消耗和运营成本。开发的支持机器学习的预测建模技术可以帮助从历史运营和维护数据中构建预测模型，并且可以应用于其他应用领域，例如输油管道系统监控和高速列车预测。