Abstract
Automated controlled process is vulnerable to faults. Faults can be amplified by the closed loop control systems and they can develop into malfunction of the loop. The closed loop may alternatively hide a fault from being observed until eventually developed to a state where loop failure is inevitable. A control loop failure will easily cause production stop or malfunction at a plant. A way to achieve a stable and effective automated system is to support online fault detection and isolation through equipment- reliability studies. The objective of this paper is to present a framework to support fault diagnosis through reliability-equipment studies. The main idea is to employ Eigen Value Analysis (EVA) and Importance Analysis (IA) to provide insight on equipment performance. The equipment of offshore industries is considered according to OREDA classification. At first EVA is used for analyzing the performance of the equipment and introducing reliability as the most prominent technical performance character. IA is then performed to develop equipment reliability analysis and classify their components based on the Component Criticality Measures (CCM). The analysis of equipment reliability can ferret out the leading causes and common-cause events to pave a way toward knowledge acquisition which enhance online fault diagnosis performance.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the SICE Annual Conference |
| Pages | 53-58 |
| Number of pages | 6 |
| Publication status | Published - 2005 |
| Event | SICE Annual Conference 2005 - Okayama, Japan Duration: Aug 8 2005 → Aug 10 2005 |
Other
| Other | SICE Annual Conference 2005 |
|---|---|
| Country | Japan |
| City | Okayama |
| Period | 8/8/05 → 8/10/05 |
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Keywords
- Dependability analysis
- Eigen value analysis
- Equipment-reliability knowledge
- Fault diagnosis
- Importance analysis
ASJC Scopus subject areas
- Engineering(all)
Cite this
Fault detection and isolation in automated controlled process based on equipment-reliability studies. / Ebrahimipour, Vahid; Gabbar, Hossam A.; Suzuki, Kazuhiko.
Proceedings of the SICE Annual Conference. 2005. p. 53-58.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Fault detection and isolation in automated controlled process based on equipment-reliability studies
AU - Ebrahimipour, Vahid
AU - Gabbar, Hossam A.
AU - Suzuki, Kazuhiko
PY - 2005
Y1 - 2005
N2 - Automated controlled process is vulnerable to faults. Faults can be amplified by the closed loop control systems and they can develop into malfunction of the loop. The closed loop may alternatively hide a fault from being observed until eventually developed to a state where loop failure is inevitable. A control loop failure will easily cause production stop or malfunction at a plant. A way to achieve a stable and effective automated system is to support online fault detection and isolation through equipment- reliability studies. The objective of this paper is to present a framework to support fault diagnosis through reliability-equipment studies. The main idea is to employ Eigen Value Analysis (EVA) and Importance Analysis (IA) to provide insight on equipment performance. The equipment of offshore industries is considered according to OREDA classification. At first EVA is used for analyzing the performance of the equipment and introducing reliability as the most prominent technical performance character. IA is then performed to develop equipment reliability analysis and classify their components based on the Component Criticality Measures (CCM). The analysis of equipment reliability can ferret out the leading causes and common-cause events to pave a way toward knowledge acquisition which enhance online fault diagnosis performance.
AB - Automated controlled process is vulnerable to faults. Faults can be amplified by the closed loop control systems and they can develop into malfunction of the loop. The closed loop may alternatively hide a fault from being observed until eventually developed to a state where loop failure is inevitable. A control loop failure will easily cause production stop or malfunction at a plant. A way to achieve a stable and effective automated system is to support online fault detection and isolation through equipment- reliability studies. The objective of this paper is to present a framework to support fault diagnosis through reliability-equipment studies. The main idea is to employ Eigen Value Analysis (EVA) and Importance Analysis (IA) to provide insight on equipment performance. The equipment of offshore industries is considered according to OREDA classification. At first EVA is used for analyzing the performance of the equipment and introducing reliability as the most prominent technical performance character. IA is then performed to develop equipment reliability analysis and classify their components based on the Component Criticality Measures (CCM). The analysis of equipment reliability can ferret out the leading causes and common-cause events to pave a way toward knowledge acquisition which enhance online fault diagnosis performance.
KW - Dependability analysis
KW - Eigen value analysis
KW - Equipment-reliability knowledge
KW - Fault diagnosis
KW - Importance analysis
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UR - http://www.scopus.com/inward/citedby.url?scp=33645281675&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:33645281675
SP - 53
EP - 58
BT - Proceedings of the SICE Annual Conference
ER -