Fault diagnosis based on set membership identification using output-error models

V. Reppa, Antonios Tzes

    Research output: Contribution to journalArticle

    Abstract

    The objective of this article is the design of a fault diagnosis method based on set membership identification for systems subject to abrupt parametric faults. The proposed method assumes an output-error, linearly parametrizable model, with unknown but bounded noise corrupting the measurement data. The set membership identification's objective is to compute (a) the smallest volume-wise ellipsoid that contains the nominal parameter vector, which concurrently resides within a data-hypersector and (b) the monotonically nonincreasing parametric set arisen from the orthotopes that tightly outer bound the ellipsoids. The fault detection mechanism is based on consistency tests between the estimated ellipsoids and the data-hypersectors and the intersection of support orthotopes. At the sample instant of the fault detection, set-theoretic operations are applied for the subsequent fault isolation and identification. The fault isolation is based on the projections of the intersections of orthotopes, while the distance of their centers is used for fault identification. Simulations studies are used to verify the efficiency of the suggested method applied on an electrostatic microactuator subject to several abrupt failure modes.

    Original languageEnglish (US)
    Pages (from-to)224-255
    Number of pages32
    JournalInternational Journal of Adaptive Control and Signal Processing
    Volume30
    Issue number2
    DOIs
    StatePublished - Feb 1 2016

    Fingerprint

    Fault detection
    Failure analysis
    Identification (control systems)
    Microactuators
    Failure modes
    Electrostatics

    Keywords

    • electrostatic microactuator
    • fault detection
    • fault identification
    • fault isolation
    • set membership identification

    ASJC Scopus subject areas

    • Control and Systems Engineering
    • Signal Processing
    • Electrical and Electronic Engineering

    Cite this

    Fault diagnosis based on set membership identification using output-error models. / Reppa, V.; Tzes, Antonios.

    In: International Journal of Adaptive Control and Signal Processing, Vol. 30, No. 2, 01.02.2016, p. 224-255.

    Research output: Contribution to journalArticle

    @article{4acadf6782a1424ba3dc3fb3009d0b93,
    title = "Fault diagnosis based on set membership identification using output-error models",
    abstract = "The objective of this article is the design of a fault diagnosis method based on set membership identification for systems subject to abrupt parametric faults. The proposed method assumes an output-error, linearly parametrizable model, with unknown but bounded noise corrupting the measurement data. The set membership identification's objective is to compute (a) the smallest volume-wise ellipsoid that contains the nominal parameter vector, which concurrently resides within a data-hypersector and (b) the monotonically nonincreasing parametric set arisen from the orthotopes that tightly outer bound the ellipsoids. The fault detection mechanism is based on consistency tests between the estimated ellipsoids and the data-hypersectors and the intersection of support orthotopes. At the sample instant of the fault detection, set-theoretic operations are applied for the subsequent fault isolation and identification. The fault isolation is based on the projections of the intersections of orthotopes, while the distance of their centers is used for fault identification. Simulations studies are used to verify the efficiency of the suggested method applied on an electrostatic microactuator subject to several abrupt failure modes.",
    keywords = "electrostatic microactuator, fault detection, fault identification, fault isolation, set membership identification",
    author = "V. Reppa and Antonios Tzes",
    year = "2016",
    month = "2",
    day = "1",
    doi = "10.1002/acs.2537",
    language = "English (US)",
    volume = "30",
    pages = "224--255",
    journal = "International Journal of Adaptive Control and Signal Processing",
    issn = "0890-6327",
    publisher = "John Wiley and Sons Ltd",
    number = "2",

    }

    TY - JOUR

    T1 - Fault diagnosis based on set membership identification using output-error models

    AU - Reppa, V.

    AU - Tzes, Antonios

    PY - 2016/2/1

    Y1 - 2016/2/1

    N2 - The objective of this article is the design of a fault diagnosis method based on set membership identification for systems subject to abrupt parametric faults. The proposed method assumes an output-error, linearly parametrizable model, with unknown but bounded noise corrupting the measurement data. The set membership identification's objective is to compute (a) the smallest volume-wise ellipsoid that contains the nominal parameter vector, which concurrently resides within a data-hypersector and (b) the monotonically nonincreasing parametric set arisen from the orthotopes that tightly outer bound the ellipsoids. The fault detection mechanism is based on consistency tests between the estimated ellipsoids and the data-hypersectors and the intersection of support orthotopes. At the sample instant of the fault detection, set-theoretic operations are applied for the subsequent fault isolation and identification. The fault isolation is based on the projections of the intersections of orthotopes, while the distance of their centers is used for fault identification. Simulations studies are used to verify the efficiency of the suggested method applied on an electrostatic microactuator subject to several abrupt failure modes.

    AB - The objective of this article is the design of a fault diagnosis method based on set membership identification for systems subject to abrupt parametric faults. The proposed method assumes an output-error, linearly parametrizable model, with unknown but bounded noise corrupting the measurement data. The set membership identification's objective is to compute (a) the smallest volume-wise ellipsoid that contains the nominal parameter vector, which concurrently resides within a data-hypersector and (b) the monotonically nonincreasing parametric set arisen from the orthotopes that tightly outer bound the ellipsoids. The fault detection mechanism is based on consistency tests between the estimated ellipsoids and the data-hypersectors and the intersection of support orthotopes. At the sample instant of the fault detection, set-theoretic operations are applied for the subsequent fault isolation and identification. The fault isolation is based on the projections of the intersections of orthotopes, while the distance of their centers is used for fault identification. Simulations studies are used to verify the efficiency of the suggested method applied on an electrostatic microactuator subject to several abrupt failure modes.

    KW - electrostatic microactuator

    KW - fault detection

    KW - fault identification

    KW - fault isolation

    KW - set membership identification

    UR - http://www.scopus.com/inward/record.url?scp=84956480188&partnerID=8YFLogxK

    UR - http://www.scopus.com/inward/citedby.url?scp=84956480188&partnerID=8YFLogxK

    U2 - 10.1002/acs.2537

    DO - 10.1002/acs.2537

    M3 - Article

    VL - 30

    SP - 224

    EP - 255

    JO - International Journal of Adaptive Control and Signal Processing

    JF - International Journal of Adaptive Control and Signal Processing

    SN - 0890-6327

    IS - 2

    ER -