TY - JOUR
T1 - Causality assignment and model approximation for hybrid bond graph
T2 - Fault diagnosis perspectives
AU - Low, Chang Boon
AU - Wang, Danwei
AU - Arogeti, Shai
AU - Zhang, Jing Bing
N1 - Funding Information:
Manuscript received February 10, 2009; revised May 11, 2009; accepted May 27, 2009. Date of publication September 25, 2009; date of current version July 02, 2010. This paper was recommended for publication by Associate Editor B. Turchiano and Editor Y. Narahari upon evaluation of the reviewers’ comments. This work was supported in part by A*Star SERC Grant 0521160078. C. B. Low is with DSO National Laboratories (Kent Ridge), Singapore 117510 (e-mail: [email protected]). D. Wang is with the Division of Control and Instrumentation, School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798 (e-mail: [email protected]). S. Arogeti is with Department of Mechanical Engineering, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel (e-mail: [email protected]). J. B. Zhang is with SIMTech A*Star Research Agency, Singapore (e-mail: [email protected]). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TASE.2009.2026731
PY - 2010/7/1
Y1 - 2010/7/1
N2 - Bond graph (BG) is an effective tool for modeling complex systems and it has been proven useful for fault detection and isolation (FDI) for continuous systems. BG provides the causal relations between system's variables which allow FDI algorithms to be developed systematically from the graph. In the same spirit, Hybrid bond graph (HBG) is a BG-based modeling approach which provides an avenue to model complex hybrid systems. However, due to mode-varying causality properties of HBG, HBG has not been efficiently-exploited for fault diagnosis. In this work, a comprehensive study on the HBG from FDI viewpoints is presented. Some properties pertaining to the HBG are gained in the study. Based on these findings, a causality assignment procedure and a model approximation technique are developed to achieve a HBG with a desirable causality assignment that leads a unified description of system's behavior. These results lay a foundation for quantitative FDI design for complex hybrid systems.
AB - Bond graph (BG) is an effective tool for modeling complex systems and it has been proven useful for fault detection and isolation (FDI) for continuous systems. BG provides the causal relations between system's variables which allow FDI algorithms to be developed systematically from the graph. In the same spirit, Hybrid bond graph (HBG) is a BG-based modeling approach which provides an avenue to model complex hybrid systems. However, due to mode-varying causality properties of HBG, HBG has not been efficiently-exploited for fault diagnosis. In this work, a comprehensive study on the HBG from FDI viewpoints is presented. Some properties pertaining to the HBG are gained in the study. Based on these findings, a causality assignment procedure and a model approximation technique are developed to achieve a HBG with a desirable causality assignment that leads a unified description of system's behavior. These results lay a foundation for quantitative FDI design for complex hybrid systems.
KW - Causality assignment
KW - fault diagnosis perspective
KW - hybrid bond graph (HBG)
KW - hybrid systems
UR - http://www.scopus.com/inward/record.url?scp=77954384431&partnerID=8YFLogxK
U2 - 10.1109/TASE.2009.2026731
DO - 10.1109/TASE.2009.2026731
M3 - Article
AN - SCOPUS:77954384431
SN - 1545-5955
VL - 7
SP - 570
EP - 580
JO - IEEE Transactions on Automation Science and Engineering
JF - IEEE Transactions on Automation Science and Engineering
IS - 3
M1 - 5262981
ER -