Smart building creation in large scale HVAC environments through automated fault detection and diagnosis
Smart building creation in large scale HVAC environments through automated fault detection and diagnosis
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DOI：
10.1016/j.future.2018.02.019
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发表时间：
2020-07-01
影响因子：
7.5
通讯作者：
Dudley, Sandra
中科院分区：
文献类型：
--
作者：
Dey, Maitreyee;Rana, Soumya Prakash;Dudley, Sandra
旧建筑的现代化和翻新推动了安装建筑能源管理系统(BEMS)的努力，该系统可以帮助建筑管理人员为更智能地使用能源铺平道路，并通过适当的方法间接地了解居住者的舒适度。BEMS可能会发现问题，这些问题可以告知管理人员建筑物维护和能源浪费问题，并通过重复的数据模式间接地了解用户的舒适度要求。本文的主要目的是描述一种检测暖通空调末端装置(TU)故障并对其进行远程自动诊断的方法。为此，构建了一个典型的大数据框架来处理非常大的数据量。提出了一种新的基于比例积分导数控制器的特征提取方法，用于描述多维TU数据流中的事件。使用无监督数据驱动策略进一步利用这些特征对不同的TU行为进行分类，并应用有监督学习来诊断故障。X-均值聚类被用来对不同的TU行为进行分组，这些TU行为在每日、每周、每月和随机选择的数据集上进行实验。随后，采用基于分类信息的多类支持向量机(MC-SVM)生成了一个自动故障检测与诊断系统，旨在使建筑变得更加智能化。聚类和分类结果进一步与已知算法和已有算法进行了比较，并通过统计测量进行了验证。(C)2018爱思唯尔B.V.保留所有权利。
Modernization and retrofitting of older buildings has created a drive to install Building Energy Management Systems (BEMS) that can assist building managers in paving the way for smarter energy use and indirectly, using appropriate methods, occupant comfort understanding. BEMS may discover problems that can inform managers of building maintenance and energy wastage issues and in-directly, via repetitive data patterns appreciate user comfort requirements. The main focus of this paper is to describe a method to detect faulty Heating, Ventilation and Air-Conditioning (HVAC) Terminal Unit (TU) and diagnose them in an automatic and remote manner. For this purpose, a typical big-data framework has been constructed to process the very large volume of data. A novel feature extraction method encouraged by Proportional Integral Derivative (PID) controller has been proposed to describe events from multidimensional TU data streams. These features are further used to categorize different TU behaviours using unsupervised data-driven strategy and supervised learning is applied to diagnose faults. X-Means clustering has been performed to group diverse TU behaviours which are experimented on daily, weekly, monthly and randomly selected dataset. Subsequently, Multi-Class Support Vector Machine (MC-SVM) has been employed based on categorical information to generate an automated fault detection and diagnosis system towards making the building smarter. The clustering and classification results further compared with well-known and established algorithms and validated through statistical measurements. (C) 2018 Elsevier B.V. All rights reserved.