TY - JOUR
T1 - Quantitative hybrid bond graph-based fault detection and isolation
AU - Low, Chang Boon
AU - Wang, Danwei
AU - Arogeti, Shai
AU - Luo, Ming
N1 - Funding Information:
Manuscript received March 12, 2009; accepted May 01, 2009. Date of publication August 18, 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 Research Agency under SERC Grant 0521160078. D. Wang is with the School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798 (e-mail: edwwang@ ntu.edu.sg). C. B. Low is with the DSO National Laboratories (Kent Ridge), Singapore 117510 (e-mail: [email protected]). S. Arogeti is with the Department of Mechanical Engineering, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel (e-mail: [email protected]). M. Luo 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.2009.2024538
PY - 2010/7/1
Y1 - 2010/7/1
N2 - This research result consists of two parts: one is general theory on causality assignment for hybrid bond graph (HBG) and another is application of this concept to the quantitative fault diagnosis. From Low et al., 2008, a foundation for quantitative bond graph-based fault detection and isolation (FDI) design using HBG is laid. Useful causality properties pertaining to the HBG from FDI perspectives, and the concept of diagnostic hybrid bond graph (DHBG) which is advantageous for efficient and effective FDI applications are proposed. This paper is a continuation of our previous paper (Low et al., 2008). Here, the DHBG is exploited to analyze the hybrid system's fault detectability and fault isolability. Additionally, a quantitative FDI framework for effective fault diagnosis for hybrid systems is proposed. Simulation and experimental results are presented to validate some key concepts of the quantitative hybrid bond graph-based FDI framework.
AB - This research result consists of two parts: one is general theory on causality assignment for hybrid bond graph (HBG) and another is application of this concept to the quantitative fault diagnosis. From Low et al., 2008, a foundation for quantitative bond graph-based fault detection and isolation (FDI) design using HBG is laid. Useful causality properties pertaining to the HBG from FDI perspectives, and the concept of diagnostic hybrid bond graph (DHBG) which is advantageous for efficient and effective FDI applications are proposed. This paper is a continuation of our previous paper (Low et al., 2008). Here, the DHBG is exploited to analyze the hybrid system's fault detectability and fault isolability. Additionally, a quantitative FDI framework for effective fault diagnosis for hybrid systems is proposed. Simulation and experimental results are presented to validate some key concepts of the quantitative hybrid bond graph-based FDI framework.
KW - Causality assignment
KW - fault diagnosis design
KW - hybrid bond graph (HBG)
KW - hybrid systems
KW - quantitative
UR - http://www.scopus.com/inward/record.url?scp=77954386690&partnerID=8YFLogxK
U2 - 10.1109/TASE.2009.2024538
DO - 10.1109/TASE.2009.2024538
M3 - Article
AN - SCOPUS:77954386690
SN - 1545-5955
VL - 7
SP - 558
EP - 569
JO - IEEE Transactions on Automation Science and Engineering
JF - IEEE Transactions on Automation Science and Engineering
IS - 3
M1 - 5204121
ER -