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Diagnosis of Hybrid Dynamic Systems Based on the Behavior Automaton Abstraction

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Fault Diagnosis of Hybrid Dynamic and Complex Systems

Abstract

This chapter focuses on the use of the hybrid automaton framework to develop a method for diagnosing hybrid systems. A hybrid automaton models the behavior of the system through a set of operation modes and a set of transitions between modes which trigger upon discrete events or based on continuous state conditions. Continuous dynamics within each mode are described by a set of differential equations which constrain the continuous state, input and output variables. The discrete event part constrains the possible transitions among modes and is referred to as the underlying DES. The restriction of the hybrid system to the continuously-valued part of the model is defined as the multimode system. The diagnosis method relies on abstracting the continuous dynamics by defining a set of “distinguishability-aware” events, called signature-events, associated to mode signature changes across modes. Signature-events are used to enrich appropriately the underlying DES to obtain the so-called behavior automaton from which a diagnoser can be built following standard methods of the discrete event system field. The diagnostic task involves detecting and isolating two types of faults: structural and non-structural faults. Structural faults are represented by a dynamic model as in the case of nominal modes and they are identified thanks to the diagnoser. Non-structural faults do not change the structure of the model in a given operation mode and are identified by a proper residual pattern. The proposed hybrid diagnosis method can operate in a non-incremental and an incremental manner. In the non-incremental form, algorithms are executed taking into account global models whereas in the incremental form only the useful parts of the diagnoser are built, developing the branches that are needed to explain the occurrence of incoming events. Thus, the resulting diagnoser adapts to the system operation life and is less demanding in terms of memory storage than building the full diagnoser offline. The incremental method is illustrated by the application to a case study based on a representative part of the Barcelona sewer network and its complexity is compared to the non-incremental method.

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Notes

  1. 1.

    It is assumed that there is only one transition from a given mode q i to a given mode q j .

  2. 2.

    Any residual generation method available in the literature could be used. See, for example, [9, 20].

  3. 3.

    The thresholds can be decided using any of the standard FDI threshold generation approaches [9].

  4. 4.

    The transient mode is the way to account for the hybrid automaton HA dwell time requirement [7].

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Acknowledgements

This work has been partially funded by the Spanish Government (MINECO) through the project ECOCIS (ref. DPI2013-48243-C2-1-R), by MINECO and FEDER through the project HARCRICS (ref. DPI2014-58104-R).

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Authors and Affiliations

  1. Automatic Control Department, Universitat Politècnica de Catalunya (UPC), Terrassa, Spain

    Ramon Sarrate & Vicenç Puig

  2. Laboratoire d’Analyse et d’Architecture des Systèmes, Centre National de la Recherche Scientifique (LAAS-CNRS), Université de Toulouse, Toulouse, France

    Louise Travé-Massuyès

Authors
  1. Ramon Sarrate
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  2. Vicenç Puig
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Correspondence to Vicenç Puig .

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  1. Institute Mines-Telecom Lille Douai, Douai, France

    Moamar Sayed-Mouchaweh

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Sarrate, R., Puig, V., Travé-Massuyès, L. (2018). Diagnosis of Hybrid Dynamic Systems Based on the Behavior Automaton Abstraction. In: Sayed-Mouchaweh, M. (eds) Fault Diagnosis of Hybrid Dynamic and Complex Systems. Springer, Cham. https://doi.org/10.1007/978-3-319-74014-0_10

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  • DOI: https://doi.org/10.1007/978-3-319-74014-0_10

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