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Model-based Health Monitoring of Hybrid Systems pp 81–146Cite as

Quantitative Hybrid Bond Graph-Based Fault Detection and Isolation

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Abstract

To perform model based fault diagnosis, a behavior model using available sensor measurements and system parameters is built to derive Analytical Redundancy Relationships (ARRs) to monitor the system. In this chapter, first, the bond graph based fault diagnosis for continuous system using ARRs is introduced. Several key steps of decision making are discussed. Two existing Bond Graph (BG)-based methods to generate symbolic ARRs, i.e., covering path method and causality inversion method, are introduced. Some useful properties of these two existing methods are presented. These insights lead to an integrated strategy which combines the gists of both symbolic ARR generation methods to derive optimum number of symbolic ARRs in an orderly and efficient fashion from the BG. In this chapter, the ARR method has been extended to the hybrid systems based on a new concept of Global Analytical Redundancy Relations (GARRs) which are unified relations to represent the monitored hybrid systems normal dynamics at all modes. In order to avoid causality reassignment due to changes in operating modes, two causality assignment methods are introduced to derive a HBG with a desirable causality assignment that leads to a unified description of system’s behavior (i.e., GARRs). These results lay a foundation for quantitative fault diagnosis design for complex hybrid systems.

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Authors and Affiliations

  1. School of Electrical and Electronic Engine, Nanyang Technological University, Nanyang Avenue, Singapore, 639798, Singapore

    Danwei Wang, Ming Yu & Chang Boon Low

  2. Department of Mechanical Engineering, Ben-Gurion University of the Negev, Soref Building, 84105, Beer Sheva, Israel

    Shai Arogeti

Authors
  1. Danwei Wang
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  2. Ming Yu
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  3. Chang Boon Low
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  4. Shai Arogeti
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Correspondence to Danwei Wang .

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Wang, D., Yu, M., Low, C., Arogeti, S. (2013). Quantitative Hybrid Bond Graph-Based Fault Detection and Isolation. In: Model-based Health Monitoring of Hybrid Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7369-5_3

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  • DOI: https://doi.org/10.1007/978-1-4614-7369-5_3

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