Abstract
| Original language | English |
|---|---|
| Title of host publication | ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference |
| Subtitle of host publication | 36th Computers and Information in Engineering Conference |
| Publisher | American Society of Mechanical Engineers ASME |
| Number of pages | 10 |
| Volume | 1B |
| ISBN (Print) | 978-0-7918-5008-4 |
| DOIs | |
| Publication status | Published - 2016 |
| MoE publication type | A4 Article in a conference publication |
| Event | 36th Computers and Information in Engineering Conference, IDETC/CIE 2016 - Charlotte, United States Duration: 21 Aug 2016 → 24 Aug 2016 |
Conference
| Conference | 36th Computers and Information in Engineering Conference, IDETC/CIE 2016 |
|---|---|
| Abbreviated title | IDETC/CIE 2016 |
| Country | United States |
| City | Charlotte |
| Period | 21/08/16 → 24/08/16 |
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Keywords
- Modeling
- Testing
- Flaw detection
Cite this
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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 proceeding › Conference article in proceedings › Scientific › peer-review
TY - GEN
T1 - A functional modelling based methodology for testing the predictions of fault detection and identification systems
AU - Papakonstantinou, Nikolaos
AU - Proper, Scott
AU - Van Bossuyt, Douglas L.
AU - O'Halloran, Bryan
AU - Tumer, Irem Y.
PY - 2016
Y1 - 2016
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 -