Diagnosis of Hybrid Systems Using Structural Model Decomposition

  • Matthew J. Daigle
  • Anibal Bregon
  • Indranil Roychoudhury
Chapter

Abstract

As engineering systems increase in complexity, it becomes more crucial to have automated fault diagnosis to ensure that system faults and failures can be quickly isolated and identified, and appropriate responses quickly executed. Complex systems are also increasingly hybrid, that is, they exhibit mixed discrete and continuous behavior. Faults in these system can manifest in both the continuous dynamics (e.g., as system parameter changes) and the discrete dynamics (e.g., as changes in component operational modes). In such systems, the complexity of fault diagnosis increases significantly. Due to the large number of possible system modes, and possible mode changes, including those that may occur during fault diagnosis, most available diagnosis approaches become prohibitively computationally expensive. This chapter develops a qualitative fault isolation framework for diagnosis of hybrid systems, based on the analysis of residual signals, and under bounded observation delay. Central to the approach is the concept of structural model decomposition, which essentially defines several smaller independent diagnosis problems that become more efficient to solve than the global system-level diagnosis problem. As a result, the developed methodology is efficient and scalable. The approach is applied to an electrical power system testbed, and simulation results demonstrate the efficacy of the approach and the computational advantages provided by structural model decomposition.

Notes

Acknowledgements

Matthew Daigle and Indranil Roychoudhury’s work has been partially supported by the NASA SMART-NAS project in the Airspace Operations and Safety Program of the Aeronautics Mission Directorate. Anibal Begon’s work has been funded by the Spanish MINECO DPI2013-45414-R grant.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Matthew J. Daigle
    • 1
  • Anibal Bregon
    • 2
  • Indranil Roychoudhury
    • 3
  1. 1.NIO USA, Inc.San JoseUSA
  2. 2.Departamento de InformáticaUniversidad de ValladolidValladolidSpain
  3. 3.Stinger Ghaffarian Technologies, Inc.NASA Ames Research Center, Moffett FieldMountain ViewUSA

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