دانلود مقاله انگلیسی رایگان:طراحی مدلهای یادگیری ماشینی با کارشناسان دامنه برای انتخاب سنسور خودکار برای تشخیص خطای انرژی - 2019
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  • Design of machine learning models with domain experts for automated sensor selection for energy fault detection Design of machine learning models with domain experts for automated sensor selection for energy fault detection
    Design of machine learning models with domain experts for automated sensor selection for energy fault detection

    سال انتشار:

    2019


    عنوان انگلیسی مقاله:

    Design of machine learning models with domain experts for automated sensor selection for energy fault detection


    ترجمه فارسی عنوان مقاله:

    طراحی مدلهای یادگیری ماشینی با کارشناسان دامنه برای انتخاب سنسور خودکار برای تشخیص خطای انرژی


    منبع:

    Sciencedirect - Elsevier - Applied Energy, 235 (2019) 117-128: doi:10:1016/j:apenergy:2018:10:107


    نویسنده:

    R.L. Hua,⁎,1, J. Grandersonc, D.M. Auslanderb, A. Agoginob


    چکیده انگلیسی:

    Data-driven techniques that extract insights from sensor data reduce the cost of improving system energy performance through fault detection and system health monitoring. To lower cost barriers to widespread deployment, a methodology is proposed that takes advantage of existing sensor data, encodes expert knowledge about the application system to create ‘virtual sensors’, and applies statistical and mathematical methods to reduce the time required for manual configurations. The approach combines sensor data points with encoded expert knowledge that is generic to the application system but independent of a particular deployment, thereby reducing the need to tailor to individual deployments. This paper not only presents a method that detects faults from measured energy data, but also (1) describes an engagement method with experts in the energy system domain to identify data, (2) integrates domain knowledge with the data, (3) automatically selects from among the large pool of potential input data, and (4) uses machine learning to automatically build a data-driven fault detection model. Demonstration on a commercial building chiller plant shows that only a small number of virtual sensors is necessary for fault detection with high accuracy rates. This corresponds to the use of only five out of 52 original sensor data points features. With as few as four features, classification F1 scores exceed 90% on the training set and 80% on the testing set. The results are implementable and realizable using off-the-shelf tools. The goal is to design with domain experts an energy monitoring system that can be configured once and then widely deployed with little additional cost or effort
    Keywords: Machine learning | Domain knowledge | Time series | Fault detection | Anomaly detection | Energy savings | Energy efficiency


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 12
    حجم فایل: 1277 کیلوبایت

    قیمت: رایگان


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