A functional modelling based methodology for testing the predictions of fault detection and identification systems

Nikolaos Papakonstantinou, Scott Proper, Douglas L. Van Bossuyt, Bryan O'Halloran, Irem Y. Tumer

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

    Abstract

    Fault detection and identification (FDI) systems, which are based on data mining and artificial intelligence techniques, cannot guarantee a perfect success rate or provide analytical proof for their predictions. This characteristic is problematic when such an FDI system is monitoring a safety-critical process. In these cases, the predictions of the FDI system need to be verified by other means, such as tests on the process, to increase trust in the diagnosis. This paper contributes an extension of the Hierarchical Functional Fault Detection and Identification (HFFDI) system, a combination of a plant-wide and multiple function-specific FDI modules, developed in past research. A test preparation and test-based verification phase is added to the HFFDI methodology. The functional decomposition of the process and the type of the faulty components guides the preparation of specific tests for every fault to be identifiable by the HFFDI system. These tests have the potential to confirm or disprove the existence of the fault(s) in the target process. The target is minor automation faults in redundant systems of the monitored process. The proposed extension of the HFFDI system is applied to a case study of a generic Nuclear Power Plant model. Two HFFDI predictions are tested (a successful and an incorrect prediction) in single fault scenarios and one prediction is tested in a in a two fault scenario. The results of the case study show that the testing phase introduced in this paper is able to confirm correct fault predictions and reject incorrect fault predictions, thus the HFFDI extension presented here improves the confidence of the HFFDI output.
    Original languageEnglish
    Title of host publicationASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
    Subtitle of host publication36th Computers and Information in Engineering Conference
    PublisherAmerican Society of Mechanical Engineers ASME
    Number of pages10
    Volume1B
    ISBN (Print)978-0-7918-5008-4
    DOIs
    Publication statusPublished - 2016
    MoE publication typeA4 Article in a conference publication
    Event36th Computers and Information in Engineering Conference, IDETC/CIE 2016 - Charlotte, United States
    Duration: 21 Aug 201624 Aug 2016

    Conference

    Conference36th Computers and Information in Engineering Conference, IDETC/CIE 2016
    Abbreviated titleIDETC/CIE 2016
    CountryUnited States
    CityCharlotte
    Period21/08/1624/08/16

    Fingerprint

    Fault detection
    Identification (control systems)
    Testing
    Nuclear power plants
    Artificial intelligence
    Data mining
    Automation
    Decomposition
    Monitoring

    Keywords

    • Modeling
    • Testing
    • Flaw detection

    Cite this

    Papakonstantinou, N., Proper, S., Van Bossuyt, D. L., O'Halloran, B., & Tumer, I. Y. (2016). A functional modelling based methodology for testing the predictions of fault detection and identification systems. In ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference: 36th Computers and Information in Engineering Conference (Vol. 1B). [DETC2016-59916] American Society of Mechanical Engineers ASME. https://doi.org/10.1115/DETC2016-59916
    Papakonstantinou, Nikolaos ; Proper, Scott ; Van Bossuyt, Douglas L. ; O'Halloran, Bryan ; Tumer, Irem Y. / A functional modelling based methodology for testing the predictions of fault detection and identification systems. ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference: 36th Computers and Information in Engineering Conference. Vol. 1B American Society of Mechanical Engineers ASME, 2016.
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    abstract = "Fault detection and identification (FDI) systems, which are based on data mining and artificial intelligence techniques, cannot guarantee a perfect success rate or provide analytical proof for their predictions. This characteristic is problematic when such an FDI system is monitoring a safety-critical process. In these cases, the predictions of the FDI system need to be verified by other means, such as tests on the process, to increase trust in the diagnosis. This paper contributes an extension of the Hierarchical Functional Fault Detection and Identification (HFFDI) system, a combination of a plant-wide and multiple function-specific FDI modules, developed in past research. A test preparation and test-based verification phase is added to the HFFDI methodology. The functional decomposition of the process and the type of the faulty components guides the preparation of specific tests for every fault to be identifiable by the HFFDI system. These tests have the potential to confirm or disprove the existence of the fault(s) in the target process. The target is minor automation faults in redundant systems of the monitored process. The proposed extension of the HFFDI system is applied to a case study of a generic Nuclear Power Plant model. Two HFFDI predictions are tested (a successful and an incorrect prediction) in single fault scenarios and one prediction is tested in a in a two fault scenario. The results of the case study show that the testing phase introduced in this paper is able to confirm correct fault predictions and reject incorrect fault predictions, thus the HFFDI extension presented here improves the confidence of the HFFDI output.",
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    Papakonstantinou, N, Proper, S, Van Bossuyt, DL, O'Halloran, B & Tumer, IY 2016, A functional modelling based methodology for testing the predictions of fault detection and identification systems. in ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference: 36th Computers and Information in Engineering Conference. vol. 1B, DETC2016-59916, American Society of Mechanical Engineers ASME, 36th Computers and Information in Engineering Conference, IDETC/CIE 2016, Charlotte, United States, 21/08/16. https://doi.org/10.1115/DETC2016-59916

    A functional modelling based methodology for testing the predictions of fault detection and identification systems. / Papakonstantinou, Nikolaos; Proper, Scott; Van Bossuyt, Douglas L.; O'Halloran, Bryan; Tumer, Irem Y.

    ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference: 36th Computers and Information in Engineering Conference. Vol. 1B American Society of Mechanical Engineers ASME, 2016. DETC2016-59916.

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

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    AU - Papakonstantinou, Nikolaos

    AU - Proper, Scott

    AU - Van Bossuyt, Douglas L.

    AU - O'Halloran, Bryan

    AU - Tumer, Irem Y.

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    N2 - Fault detection and identification (FDI) systems, which are based on data mining and artificial intelligence techniques, cannot guarantee a perfect success rate or provide analytical proof for their predictions. This characteristic is problematic when such an FDI system is monitoring a safety-critical process. In these cases, the predictions of the FDI system need to be verified by other means, such as tests on the process, to increase trust in the diagnosis. This paper contributes an extension of the Hierarchical Functional Fault Detection and Identification (HFFDI) system, a combination of a plant-wide and multiple function-specific FDI modules, developed in past research. A test preparation and test-based verification phase is added to the HFFDI methodology. The functional decomposition of the process and the type of the faulty components guides the preparation of specific tests for every fault to be identifiable by the HFFDI system. These tests have the potential to confirm or disprove the existence of the fault(s) in the target process. The target is minor automation faults in redundant systems of the monitored process. The proposed extension of the HFFDI system is applied to a case study of a generic Nuclear Power Plant model. Two HFFDI predictions are tested (a successful and an incorrect prediction) in single fault scenarios and one prediction is tested in a in a two fault scenario. The results of the case study show that the testing phase introduced in this paper is able to confirm correct fault predictions and reject incorrect fault predictions, thus the HFFDI extension presented here improves the confidence of the HFFDI output.

    AB - Fault detection and identification (FDI) systems, which are based on data mining and artificial intelligence techniques, cannot guarantee a perfect success rate or provide analytical proof for their predictions. This characteristic is problematic when such an FDI system is monitoring a safety-critical process. In these cases, the predictions of the FDI system need to be verified by other means, such as tests on the process, to increase trust in the diagnosis. This paper contributes an extension of the Hierarchical Functional Fault Detection and Identification (HFFDI) system, a combination of a plant-wide and multiple function-specific FDI modules, developed in past research. A test preparation and test-based verification phase is added to the HFFDI methodology. The functional decomposition of the process and the type of the faulty components guides the preparation of specific tests for every fault to be identifiable by the HFFDI system. These tests have the potential to confirm or disprove the existence of the fault(s) in the target process. The target is minor automation faults in redundant systems of the monitored process. The proposed extension of the HFFDI system is applied to a case study of a generic Nuclear Power Plant model. Two HFFDI predictions are tested (a successful and an incorrect prediction) in single fault scenarios and one prediction is tested in a in a two fault scenario. The results of the case study show that the testing phase introduced in this paper is able to confirm correct fault predictions and reject incorrect fault predictions, thus the HFFDI extension presented here improves the confidence of the HFFDI output.

    KW - Modeling

    KW - Testing

    KW - Flaw detection

    U2 - 10.1115/DETC2016-59916

    DO - 10.1115/DETC2016-59916

    M3 - Conference article in proceedings

    SN - 978-0-7918-5008-4

    VL - 1B

    BT - ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference

    PB - American Society of Mechanical Engineers ASME

    ER -

    Papakonstantinou N, Proper S, Van Bossuyt DL, O'Halloran B, Tumer IY. A functional modelling based methodology for testing the predictions of fault detection and identification systems. In ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference: 36th Computers and Information in Engineering Conference. Vol. 1B. American Society of Mechanical Engineers ASME. 2016. DETC2016-59916 https://doi.org/10.1115/DETC2016-59916