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Building fault detection and diagnostics: Achieved savings, and methods to evaluate algorithm performance

机译：建筑故障检测和诊断：储蓄，以及评估算法性能的方法

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摘要

Fault detection and diagnosis (FDD) represents one of the most active areas of research and commercial product development in the buildings industry. This paper addresses two questions concerning FDD implementation and advancement 1) What are today's users of FDD saving and spending on the technology? 2) What methods and datasets can be used to evaluate and benchmark FDD algorithm performance? Relevant to the first question, 26 organizations that use FDD across a total 550 buildings and 97 M sf achieved median savings of 8%. Twentyseven FDD users reported that the median base cost for FDD software, annual recurring software cost, and annual labor cost were $8, $2.7 and $8 per monitoring point, with a median implementation size of approximately 1300 points. To address the second question, this paper describes a systematic methodology for evaluating the performance of FDD algorithms, curates an initial test dataset of air handling unit (AHU) system faults, and completes a trial to demonstrate the evaluation process on three sample FDD algorithms. The work provided a first step toward a standard evaluation of different FDD technologies. It showed the test methodology is indeed scalable and repeatable, provided an understanding of the types of insights that can be gained from algorithm performance testing, and highlighted the priorities for further expanding the test dataset.
机译：故障检测和诊断（FDD）代表建筑业中最活跃的研究和商业产品领域之一。本文涉及有关FDD实施和进步的两个问题1）今天的FDD节省和支出技术的用户是什么？ 2）可以使用哪些方法和数据集来评估和基准测试FDD算法性能？与第一个问题有关，26个组织在共550个建筑中使用FDD，97米SF实现了8％的中位数节省。 TwentySeven FDD用户报告说，FDD软件的中位数基本成本，年度重复的软件成本和年度劳动力成本为8美元，每次监测点$ 2.7和8美元，中位数实施规约约为1300点。为了解决第二个问题，本文介绍了用于评估FDD算法性能的系统方法，策划空气处理单元（AHU）系统故障的初始测试数据集，并完成试验以演示三个样本FDD算法上的评估过程。该工作提供了对不同FDD技术的标准评估的第一步。它显示了测试方法确实可扩展且可重复，提供了对可以从算法性能测试中获得的洞察类型的理解，并突出显示进一步扩展测试数据集的优先级。

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