Quantitative Hybrid Bond Graph-Based Fault Detection and Isolation
| Autor: | Chang Boon Low, Danwei Wang, Shai Arogeti, Ming Luo |
|---|---|
| Rok vydání: | 2010 |
| Předmět: |
Engineering
Theoretical computer science business.industry System identification Complex system Condition monitoring Fault detection and isolation Continuation Control and Systems Engineering Embedded system Hybrid system Graph (abstract data type) Electrical and Electronic Engineering business Bond graph |
| Zdroj: | IEEE Transactions on Automation Science and Engineering. 7:558-569 |
| ISSN: | 1545-5955 |
| DOI: | 10.1109/tase.2009.2024538 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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