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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ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
August 2–5, 2015
Boston, Massachusetts, USA
Conference Sponsors:
- Design Engineering Division
- Computers and Information in Engineering Division
ISBN:
978-0-7918-5705-2
PROCEEDINGS PAPER
A Plant-Wide and Function-Specific Hierarchical Functional Fault Detection and Identification (HFFDI) System for Multiple Fault Scenarios on Complex Systems
Nikolaos Papakonstantinou,
Nikolaos Papakonstantinou
VTT Technical Research Centre of Finland, Espoo, Finland
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Scott Proper,
Scott Proper
Oregon State University, Corvallis, OR
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Bryan O’Halloran,
Bryan O’Halloran
Raytheon Missile Systems, Tucson, AZ
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Irem Y. Tumer
Irem Y. Tumer
Oregon State University, Corvallis, OR
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Nikolaos Papakonstantinou
VTT Technical Research Centre of Finland, Espoo, Finland
Scott Proper
Oregon State University, Corvallis, OR
Bryan O’Halloran
Raytheon Missile Systems, Tucson, AZ
Irem Y. Tumer
Oregon State University, Corvallis, OR
Paper No:
DETC2015-46447, V01BT02A039; 10 pages
Published Online:
January 19, 2016
Citation
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." Proceedings of the ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Volume 1B: 35th Computers and Information in Engineering Conference. Boston, Massachusetts, USA. August 2–5, 2015. V01BT02A039. ASME. https://doi.org/10.1115/DETC2015-46447
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