Fault detection and isolation in automated controlled process based on equipment-reliability studies

Vahid Ebrahimipour, Hossam A. Gabbar, Kazuhiko Suzuki

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish
Title of host publicationProceedings of the SICE Annual Conference
Pages53-58
Number of pages6
Publication statusPublished - 2005
EventSICE Annual Conference 2005 - Okayama, Japan
Duration: Aug 8 2005Aug 10 2005

Other

OtherSICE Annual Conference 2005
CountryJapan
CityOkayama
Period8/8/058/10/05

Fingerprint

Fault detection
Value engineering
Failure analysis
Closed loop control systems
Knowledge acquisition
Reliability analysis
Industry

Keywords

  • Dependability analysis
  • Eigen value analysis
  • Equipment-reliability knowledge
  • Fault diagnosis
  • Importance analysis

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Ebrahimipour, V., Gabbar, H. A., & Suzuki, K. (2005). Fault detection and isolation in automated controlled process based on equipment-reliability studies. In Proceedings of the SICE Annual Conference (pp. 53-58)

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 proceedingConference contribution

Ebrahimipour, V, Gabbar, HA & Suzuki, K 2005, Fault detection and isolation in automated controlled process based on equipment-reliability studies. in Proceedings of the SICE Annual Conference. pp. 53-58, SICE Annual Conference 2005, Okayama, Japan, 8/8/05.
Ebrahimipour V, Gabbar HA, Suzuki K. Fault detection and isolation in automated controlled process based on equipment-reliability studies. In Proceedings of the SICE Annual Conference. 2005. p. 53-58
Ebrahimipour, Vahid ; Gabbar, Hossam A. ; Suzuki, Kazuhiko. / Fault detection and isolation in automated controlled process based on equipment-reliability studies. Proceedings of the SICE Annual Conference. 2005. pp. 53-58
@inproceedings{8e964945a19240bc90967cd70cac186c,
title = "Fault detection and isolation in automated controlled process based on equipment-reliability studies",
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.",
keywords = "Dependability analysis, Eigen value analysis, Equipment-reliability knowledge, Fault diagnosis, Importance analysis",
author = "Vahid Ebrahimipour and Gabbar, {Hossam A.} and Kazuhiko Suzuki",
year = "2005",
language = "English",
pages = "53--58",
booktitle = "Proceedings of the SICE Annual Conference",

}

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

UR - http://www.scopus.com/inward/record.url?scp=33645281675&partnerID=8YFLogxK

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 -