Large non-residential buildings can contain complex and often inefficient water distribution systems. As requirements for water increase due to water scarcity and industrialization, it has become increasingly important to effectively detect and diagnose faults in water distribution systems in large buildings. In many cases, if water supply is not impacted, faults in water distribution systems can go unnoticed. This can lead to unnecessary increases in water usage and associated energy due to pumping, treating, and heating water. The majority of fault detection and diagnosis studies in the water sector are limited to municipal water supply and leakage detection. The application of detection and diagnosis for faults in building water networks remains largely unexplored and the ability to identify and distinguish between routine and non-routine water usage at this scale remains a challenge. This study using case-study data, presents the application of principal component analysis and a multi-class support vector machine to detect and classify faults for non-residential building water networks. In the absence of a process model (which is typical for such water distribution systems), principal component analysis is proposed as a data-driven fault detection technique for building water distribution systems for the first time herein. Hotelling T²-statistics and Q-statistics were employed to detect abnormality within incoming data, and a multi-class support vector machine was trained for fault classification. Despite the relatively limited training data available from the case-study (which would reflect the situation in many buildings), meaningful faults were detected, and the technique proved successful in discriminating between various types of faults in the water distribution system. The effectiveness of the proposed approach is compared to a univariate threshold technique by comparison of their respective performance in the detection of faults that occurred in the case-study site. The results demonstrate the promising capabilities of the proposed fault detection and diagnosis approach. Such a strategy could provide a robust methodology that can be applied to buildings to reduce inefficient water use, reducing their life-cycle carbon footprint.

大型非住宅建筑可能包含复杂且通常效率低下的配水系统。由于缺水和工业化对水的需求增加，有效检测和诊断大型建筑物中的供水系统中的故障变得越来越重要。在许多情况下，如果不影响供水，配水系统中的故障可能不会引起注意。由于抽水，处理水和加热水，这会导致不必要的用水量和相关能源增加。水行业的大多数故障检测和诊断研究仅限于市政供水和泄漏检测。在建筑物供水网络中对故障进行检测和诊断的应用仍在很大程度上尚未开发，并且在这种规模下识别和区分常规用水和非常规用水的能力仍然是一个挑战。该研究使用案例研究数据，介绍了主成分分析和多类支持向量机在非住宅建筑供水网络故障检测和分类中的应用。在没有过程模型（对于这种水分配系统而言通常是典型的）的情况下，本文首次提出将主成分分析作为一种数据驱动的故障检测技术，用于建筑水分配系统。T酒店 介绍了主成分分析和多类支持向量机在非住宅建筑供水网络故障检测和分类中的应用。在缺少过程模型（对于这种水分配系统而言通常是典型的）的情况下，本文首次提出将主成分分析作为一种数据驱动的故障检测技术，用于建筑物的水分配系统。T酒店 介绍了主成分分析和多类支持向量机在非住宅建筑供水网络故障检测和分类中的应用。在没有过程模型（对于这种水分配系统而言通常是典型的）的情况下，本文首次提出将主成分分析作为一种数据驱动的故障检测技术，用于建筑水分配系统。T酒店²统计量和Q统计量用于检测传入数据中的异常，并训练了多类支持向量机进行故障分类。尽管案例研究提供的培训数据相对有限（可以反映许多建筑物的情况），但仍可以检测到有意义的故障，并且该技术已成功地区分了供水系统中的各种类型的故障。通过比较在案例研究站点中发生的故障检测中它们各自的性能，将所提出的方法的有效性与单变量阈值技术进行了比较。结果证明了所提出的故障检测和诊断方法的有前途的功能。