YaBeSH Engineering and Technology Library

    • Journals
    • PaperQuest
    • YSE Standards
    • YaBeSH
    • Login
    View Item 
    •   YE&T Library
    • ASME
    • ASME Journal of Engineering for Sustainable Buildings and Cities
    • View Item
    •   YE&T Library
    • ASME
    • ASME Journal of Engineering for Sustainable Buildings and Cities
    • View Item
    • All Fields
    • Source Title
    • Year
    • Publisher
    • Title
    • Subject
    • Author
    • DOI
    • ISBN
    Advanced Search
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Archive

    A Time-Series Rank Ordering Control System Data-Driven Fault Detection Approach for HVAC Systems in Buildings

    Source: ASME Journal of Engineering for Sustainable Buildings and Cities:;2021:;volume( 002 ):;issue: 001::page 011004-1
    Author:
    Shia, James A.
    ,
    Auslander, David M.
    DOI: 10.1115/1.4049506
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: About 41% of total energy consumption in the United States of year 2014 is used for heating and air conditioning, which is about 40 quadrillion (40 × 1015) British thermal units (BTUs). Despite the fact that people have been working on fault detection and diagnosis (FDD) for heating, ventilation, and air conditioning (HVAC) systems for a long time, very few publications have focused on scalability and low cost. To address this challenge, we will propose an approach that focuses on control system data. Several machine learning algorithms are introduced for data exploration (e.g., DBSCAN, k-means) and analysis, rank-ordered weather data are used to define comparable days, a control system data focused model free approach is presented as well, and finally, fault detection is carried out by implementing anomaly algorithms (local outlier factor and isolation forest). Hidden faults were detected with a detection rate up to 90%+. The threshold parameter can be determined by selecting an acceptable true positive and false positive rate pair, which can be visualized using receiver operating characteristic (ROC) curves and is demonstrated in this article. A simulation model that is used to generate about 250 GB of data is used to evaluate the performance of various algorithms.
    • Download: (2.045Mb)
    • Show Full MetaData Hide Full MetaData
    • Get RIS
    • Item Order
    • Go To Publisher
    • Price: 5000 Rial
    • Statistics

      A Time-Series Rank Ordering Control System Data-Driven Fault Detection Approach for HVAC Systems in Buildings

    URI
    http://yetl.yabesh.ir/yetl1/handle/yetl/4277954
    Collections
    • ASME Journal of Engineering for Sustainable Buildings and Cities

    Show full item record

    contributor authorShia, James A.
    contributor authorAuslander, David M.
    date accessioned2022-02-05T22:40:29Z
    date available2022-02-05T22:40:29Z
    date copyright2/1/2021 12:00:00 AM
    date issued2021
    identifier issn2642-6641
    identifier otherjesbc_2_1_011004.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4277954
    description abstractAbout 41% of total energy consumption in the United States of year 2014 is used for heating and air conditioning, which is about 40 quadrillion (40 × 1015) British thermal units (BTUs). Despite the fact that people have been working on fault detection and diagnosis (FDD) for heating, ventilation, and air conditioning (HVAC) systems for a long time, very few publications have focused on scalability and low cost. To address this challenge, we will propose an approach that focuses on control system data. Several machine learning algorithms are introduced for data exploration (e.g., DBSCAN, k-means) and analysis, rank-ordered weather data are used to define comparable days, a control system data focused model free approach is presented as well, and finally, fault detection is carried out by implementing anomaly algorithms (local outlier factor and isolation forest). Hidden faults were detected with a detection rate up to 90%+. The threshold parameter can be determined by selecting an acceptable true positive and false positive rate pair, which can be visualized using receiver operating characteristic (ROC) curves and is demonstrated in this article. A simulation model that is used to generate about 250 GB of data is used to evaluate the performance of various algorithms.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleA Time-Series Rank Ordering Control System Data-Driven Fault Detection Approach for HVAC Systems in Buildings
    typeJournal Paper
    journal volume2
    journal issue1
    journal titleASME Journal of Engineering for Sustainable Buildings and Cities
    identifier doi10.1115/1.4049506
    journal fristpage011004-1
    journal lastpage011004-14
    page14
    treeASME Journal of Engineering for Sustainable Buildings and Cities:;2021:;volume( 002 ):;issue: 001
    contenttypeFulltext
    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian
     
    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian