A Plant-Wide and Function-Specific Hierarchical Functional Fault Detection and Identification (HFFDI) System for Multiple Fault Scenarios on Complex Systems

Nikolaos Papakonstantinou, Scott Proper, Bryan O'Halloran, Irem Y. Tumer

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

    2 Citations (Scopus)

    Abstract

    The development of Fault Detection and Identification (FDI) systems for complex mechatronic systems is a challenging process. Many quantitative and qualitative fault detection methods have been proposed in past literature. Few methods address multiple faults, instead an emphasis is placed on accurately proving a single fault exists. The omission of multiple faults regulates the capability of most fault detection methods. The Functional Failure Identification and Propagation (FFIP) framework has been utilized in past research for various applications related to fault propagation in complex systems. In this paper a Hierarchical Functional Fault Detection and Identification (HFFDI) system is proposed. The development of the HFFDI system is based on machine learning techniques, commonly used as a basis for FDI systems, and the functional system decomposition of the FFIP framework. The HFFDI is composed of a plant-wide FDI system and function-specific FDI systems. The HFFDI aims at fault identification in multiple fault scenarios using single fault data sets, when faults happen in different system functions. The methodology is applied to a case study of a generic nuclear power plant with 17 system functions. Compared with a plant-wide FDI system, in multiple fault scenarios the HFFDI gave better results for identifying one fault and also was able to identify more than one faults. The case study results show that in two fault scenarios the HFFDI was able to identify one of the faults with 79% accuracy and both faults with 13% accuracy. In three fault scenarios the HFFDI was able to identify one of the faults with 69% accuracy, two faults with 22% accuracy and all three faults with 1% accuracy.
    Original languageEnglish
    Title of host publicationASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
    PublisherAmerican Society of Mechanical Engineers ASME
    Number of pages10
    Volume1B
    ISBN (Electronic)978-0-7918-5705-2
    DOIs
    Publication statusPublished - 2015
    MoE publication typeA4 Article in a conference publication
    Event35th Computers and Information in Engineering Conference, IDETC/CIE 2015 - Boston, United States
    Duration: 2 Aug 20155 Aug 2015
    Conference number: 35

    Conference

    Conference35th Computers and Information in Engineering Conference, IDETC/CIE 2015
    Abbreviated titleIDETC/CIE 2015
    CountryUnited States
    CityBoston
    Period2/08/155/08/15
    OtherASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference

    Keywords

    • flaw detection
    • complex systems

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  • Cite this

    Papakonstantinou, N., Proper, S., O'Halloran, B., & Tumer, I. Y. (2015). A Plant-Wide and Function-Specific Hierarchical Functional Fault Detection and Identification (HFFDI) System for Multiple Fault Scenarios on Complex Systems. In ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (Vol. 1B). [DETC2015-46447] American Society of Mechanical Engineers ASME. https://doi.org/10.1115/DETC2015-46447