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Fault detection and diagnosis of large-scale HVAC systems in buildings using data-driven methods: A comprehensive review

机译：使用数据驱动方法的建筑物中大型HVAC系统的故障检测与诊断：全面审查

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摘要

Abnormal operation of HVAC systems can result in an increase in energy usage as well as poor indoor air quality, thermal discomfort, and low productivity. Building automated systems (BAS) collects a massive amount of data related to the operation of each component of HVAC systems. Although BAS has been implemented in many buildings over the past decade, the collected data have not been analyzed thoroughly. Some studies have relied on data-mining methods to predict, detect, and diagnose faults in HVAC systems. This paper critically reviews the existing literature and identifies the research gaps in data-driven data mining fault detection and diagnosis (FDD) methods studies on HVAC systems. In this review, data-driven based FDD methods are classified into three classes, namely supervised, unsupervised, and hybrid-learning methods. The hybrid approaches are introduced as the preferred methods among the existing approaches to be used in online FDD processes. Furthermore, some components of HVAC systems and their potential faults are discussed in detail. The outcome of this review shows that data-driven based approaches are more promising for the FDD process of large-scale HVAC systems than model-based and knowledge-based ones. Moreover, an optimal approach could involve both supervised and unsupervised learning (hybrid methods). (C) 2020 Elsevier B.V. All rights reserved.
机译：HVAC系统的异常运行可能导致能源使用量增加以及室内空气质量差，热不适和低生产率。构建自动化系统（BAS）收集与HVAC系统的每个组件的操作相关的大量数据。尽管在过去十年中，BAS已在许多建筑物中实施，但收集的数据尚未彻底分析。一些研究依赖于数据采矿方法来预测，检测和诊断HVAC系统中的故障。本文重视现有文献，并识别数据驱动数据挖掘故障检测和诊断中的研究差距（FDD）方法研究HVAC系统。在本次审查中，基于数据驱动的FDD方法分为三个类，即监督，无监督和混合学习方法。将混合方法作为在线FDD流程中使用的现有方法中的优选方法引入。此外，详细讨论了HVAC系统的一些组分及其潜在故障。本综述结果表明，基于数据驱动的方法对于大规模HVAC系统的FDD过程比基于模型和基于知识的方式更为了前途。此外，最佳方法可能涉及监督和无监督的学习（混合方法）。 （c）2020 Elsevier B.V.保留所有权利。

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