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

Fingerprint

Fault detection
Large scale systems
Identification (control systems)
Mechatronics
Nuclear power plants
Learning systems

Keywords

  • flaw detection
  • complex systems

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
Papakonstantinou, Nikolaos ; Proper, Scott ; O'Halloran, Bryan ; Tumer, Irem Y. / A Plant-Wide and Function-Specific Hierarchical Functional Fault Detection and Identification (HFFDI) System for Multiple Fault Scenarios on Complex Systems. ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Vol. 1B American Society of Mechanical Engineers ASME, 2015.
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title = "A Plant-Wide and Function-Specific Hierarchical Functional Fault Detection and Identification (HFFDI) System for Multiple Fault Scenarios on Complex Systems",
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.",
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Papakonstantinou, N, Proper, S, O'Halloran, B & Tumer, IY 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, 35th Computers and Information in Engineering Conference, IDETC/CIE 2015, Boston, United States, 2/08/15. https://doi.org/10.1115/DETC2015-46447

A Plant-Wide and Function-Specific Hierarchical Functional Fault Detection and Identification (HFFDI) System for Multiple Fault Scenarios on Complex Systems. / Papakonstantinou, Nikolaos; Proper, Scott; O'Halloran, Bryan; Tumer, Irem Y.

ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Vol. 1B American Society of Mechanical Engineers ASME, 2015. DETC2015-46447.

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

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T1 - A Plant-Wide and Function-Specific Hierarchical Functional Fault Detection and Identification (HFFDI) System for Multiple Fault Scenarios on Complex Systems

AU - Papakonstantinou, Nikolaos

AU - Proper, Scott

AU - O'Halloran, Bryan

AU - Tumer, Irem Y.

PY - 2015

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N2 - 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.

AB - 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.

KW - flaw detection

KW - complex systems

U2 - 10.1115/DETC2015-46447

DO - 10.1115/DETC2015-46447

M3 - Conference article in proceedings

VL - 1B

BT - ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference

PB - American Society of Mechanical Engineers ASME

ER -

Papakonstantinou N, Proper S, O'Halloran B, Tumer IY. 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. American Society of Mechanical Engineers ASME. 2015. DETC2015-46447 https://doi.org/10.1115/DETC2015-46447