Data-driven fault detection in nuclear power plants under sensor degradation

Xin Jin, Yin Guo, Robert M. Edwards, Asok Ray

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    Tools of data-driven fault detection facilitate performance monitoring of complex dynamical systems if the physics-based models are either inadequate or not available. To this end, many data-driven methods have been developed. An inherent difficulty for a completely data-driven fault detection tool is that the detection performance can deduce drastically in the presence of sensor degradation. Symbolic dynamic filtering (SDF) is recently introduced in the literature as a real-time data-driven pattern identification tool, which is built upon the concepts of Symbolic Dynamics, Information Theory and Statistical Mechanics. This paper investigates a SDF-based fault detection algorithm for health monitoring in nuclear power plants under sensor degradation. The proposed fault detection methodology is validated on the International Reactor Innovative & Secure (IRIS) simulator of nuclear power plants, and its performance is comparatively evaluated with existing fault detection tools.

    Original languageEnglish (US)
    Title of host publication7th International Topical Meeting on Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies 2010, NPIC and HMIT 2010
    Pages1586-1599
    Number of pages14
    Volume3
    StatePublished - 2010
    Event7th International Topical Meeting on Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies 2010, NPIC and HMIT 2010 - Las Vegas, NV, United States
    Duration: Nov 7 2010Nov 11 2010

    Other

    Other7th International Topical Meeting on Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies 2010, NPIC and HMIT 2010
    CountryUnited States
    CityLas Vegas, NV
    Period11/7/1011/11/10

    Fingerprint

    Fault detection
    Nuclear power plants
    Degradation
    Sensors
    Statistical mechanics
    Monitoring
    Information theory
    Dynamical systems
    Physics
    Simulators
    Health

    All Science Journal Classification (ASJC) codes

    • Human-Computer Interaction
    • Nuclear Energy and Engineering
    • Control and Systems Engineering

    Cite this

    Jin, X., Guo, Y., Edwards, R. M., & Ray, A. (2010). Data-driven fault detection in nuclear power plants under sensor degradation. In 7th International Topical Meeting on Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies 2010, NPIC and HMIT 2010 (Vol. 3, pp. 1586-1599)
    Jin, Xin ; Guo, Yin ; Edwards, Robert M. ; Ray, Asok. / Data-driven fault detection in nuclear power plants under sensor degradation. 7th International Topical Meeting on Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies 2010, NPIC and HMIT 2010. Vol. 3 2010. pp. 1586-1599
    @inproceedings{8d53e1a4c8ce479c9f62998243347aae,
    title = "Data-driven fault detection in nuclear power plants under sensor degradation",
    abstract = "Tools of data-driven fault detection facilitate performance monitoring of complex dynamical systems if the physics-based models are either inadequate or not available. To this end, many data-driven methods have been developed. An inherent difficulty for a completely data-driven fault detection tool is that the detection performance can deduce drastically in the presence of sensor degradation. Symbolic dynamic filtering (SDF) is recently introduced in the literature as a real-time data-driven pattern identification tool, which is built upon the concepts of Symbolic Dynamics, Information Theory and Statistical Mechanics. This paper investigates a SDF-based fault detection algorithm for health monitoring in nuclear power plants under sensor degradation. The proposed fault detection methodology is validated on the International Reactor Innovative & Secure (IRIS) simulator of nuclear power plants, and its performance is comparatively evaluated with existing fault detection tools.",
    author = "Xin Jin and Yin Guo and Edwards, {Robert M.} and Asok Ray",
    year = "2010",
    language = "English (US)",
    isbn = "9781617822667",
    volume = "3",
    pages = "1586--1599",
    booktitle = "7th International Topical Meeting on Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies 2010, NPIC and HMIT 2010",

    }

    Jin, X, Guo, Y, Edwards, RM & Ray, A 2010, Data-driven fault detection in nuclear power plants under sensor degradation. in 7th International Topical Meeting on Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies 2010, NPIC and HMIT 2010. vol. 3, pp. 1586-1599, 7th International Topical Meeting on Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies 2010, NPIC and HMIT 2010, Las Vegas, NV, United States, 11/7/10.

    Data-driven fault detection in nuclear power plants under sensor degradation. / Jin, Xin; Guo, Yin; Edwards, Robert M.; Ray, Asok.

    7th International Topical Meeting on Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies 2010, NPIC and HMIT 2010. Vol. 3 2010. p. 1586-1599.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    TY - GEN

    T1 - Data-driven fault detection in nuclear power plants under sensor degradation

    AU - Jin, Xin

    AU - Guo, Yin

    AU - Edwards, Robert M.

    AU - Ray, Asok

    PY - 2010

    Y1 - 2010

    N2 - Tools of data-driven fault detection facilitate performance monitoring of complex dynamical systems if the physics-based models are either inadequate or not available. To this end, many data-driven methods have been developed. An inherent difficulty for a completely data-driven fault detection tool is that the detection performance can deduce drastically in the presence of sensor degradation. Symbolic dynamic filtering (SDF) is recently introduced in the literature as a real-time data-driven pattern identification tool, which is built upon the concepts of Symbolic Dynamics, Information Theory and Statistical Mechanics. This paper investigates a SDF-based fault detection algorithm for health monitoring in nuclear power plants under sensor degradation. The proposed fault detection methodology is validated on the International Reactor Innovative & Secure (IRIS) simulator of nuclear power plants, and its performance is comparatively evaluated with existing fault detection tools.

    AB - Tools of data-driven fault detection facilitate performance monitoring of complex dynamical systems if the physics-based models are either inadequate or not available. To this end, many data-driven methods have been developed. An inherent difficulty for a completely data-driven fault detection tool is that the detection performance can deduce drastically in the presence of sensor degradation. Symbolic dynamic filtering (SDF) is recently introduced in the literature as a real-time data-driven pattern identification tool, which is built upon the concepts of Symbolic Dynamics, Information Theory and Statistical Mechanics. This paper investigates a SDF-based fault detection algorithm for health monitoring in nuclear power plants under sensor degradation. The proposed fault detection methodology is validated on the International Reactor Innovative & Secure (IRIS) simulator of nuclear power plants, and its performance is comparatively evaluated with existing fault detection tools.

    UR - http://www.scopus.com/inward/record.url?scp=79958297472&partnerID=8YFLogxK

    UR - http://www.scopus.com/inward/citedby.url?scp=79958297472&partnerID=8YFLogxK

    M3 - Conference contribution

    AN - SCOPUS:79958297472

    SN - 9781617822667

    VL - 3

    SP - 1586

    EP - 1599

    BT - 7th International Topical Meeting on Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies 2010, NPIC and HMIT 2010

    ER -

    Jin X, Guo Y, Edwards RM, Ray A. Data-driven fault detection in nuclear power plants under sensor degradation. In 7th International Topical Meeting on Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies 2010, NPIC and HMIT 2010. Vol. 3. 2010. p. 1586-1599