Detecting and Diagnosing Incipient Building Faults Using Uncertainty Information from Deep Neural Networks

Baihong Jin, Dan Li, S. Srinivasan, See-Kiong Ng, K. Poolla, A. Sangiovanni-Vincentelli
{"title":"Detecting and Diagnosing Incipient Building Faults Using Uncertainty Information from Deep Neural Networks","authors":"Baihong Jin, Dan Li, S. Srinivasan, See-Kiong Ng, K. Poolla, A. Sangiovanni-Vincentelli","doi":"10.1109/ICPHM.2019.8819438","DOIUrl":null,"url":null,"abstract":"Early detection of incipient faults is of vital importance to reducing maintenance costs, saving energy, and enhancing occupant comfort in buildings. Popular supervised learning models such as deep neural networks are considered promising due to their ability to directly learn from labeled fault data; however, it is known that the performance of supervised learning approaches highly relies on the availability and quality of labeled training data. In Fault Detection and Diagnosis (FDD) applications, the lack of labeled incipient fault data has posed a major challenge to applying these supervised learning techniques to commercial buildings. To overcome this challenge, this paper proposes using Monte Carlo dropout (MC-dropout) to enhance the supervised learning pipeline, so that the resulting neural network is able to detect and diagnose unseen incipient fault examples. We also examine the proposed MC-dropout method on the RP-1043 dataset to demonstrate its effectiveness in indicating the most likely incipient fault types.","PeriodicalId":113460,"journal":{"name":"2019 IEEE International Conference on Prognostics and Health Management (ICPHM)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"23","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE International Conference on Prognostics and Health Management (ICPHM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICPHM.2019.8819438","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 23

Abstract

Early detection of incipient faults is of vital importance to reducing maintenance costs, saving energy, and enhancing occupant comfort in buildings. Popular supervised learning models such as deep neural networks are considered promising due to their ability to directly learn from labeled fault data; however, it is known that the performance of supervised learning approaches highly relies on the availability and quality of labeled training data. In Fault Detection and Diagnosis (FDD) applications, the lack of labeled incipient fault data has posed a major challenge to applying these supervised learning techniques to commercial buildings. To overcome this challenge, this paper proposes using Monte Carlo dropout (MC-dropout) to enhance the supervised learning pipeline, so that the resulting neural network is able to detect and diagnose unseen incipient fault examples. We also examine the proposed MC-dropout method on the RP-1043 dataset to demonstrate its effectiveness in indicating the most likely incipient fault types.
基于深度神经网络不确定性信息的早期建筑故障检测与诊断
早期发现早期故障对于降低建筑维护成本、节约能源、提高居住者舒适度具有重要意义。流行的监督学习模型,如深度神经网络,被认为是有前途的,因为它们能够直接从标记的故障数据中学习;然而,众所周知,监督学习方法的性能高度依赖于标记训练数据的可用性和质量。在故障检测和诊断(FDD)应用中,缺乏标记的早期故障数据对将这些监督学习技术应用于商业建筑提出了主要挑战。为了克服这一挑战,本文提出使用蒙特卡罗dropout (MC-dropout)来增强监督学习管道,使所得到的神经网络能够检测和诊断未见过的早期故障示例。我们还在RP-1043数据集上检验了提出的MC-dropout方法,以证明其在指示最可能的早期故障类型方面的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信