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Diagnosis of Active Systems with Abstract Observability

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Intelligent Decision Technologies

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 238))

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Abstract

Active systems (ASs) are a special class of (asynchronous) discrete-event systems (DESs). An AS is represented by a network of components, where each component is modeled as a communicating automaton. Diagnosing a DES amounts to finding out possible faults based on the DES model and a sequence of observations gathered while the DES is being operated. This is why the diagnosis engine needs to know what is observable in the behavior of the DES and what is not. The notion of observability serves this purpose. In the literature, defining the observability of a DES boils down to qualifying the state transitions of components either as observable or unobservable, where each observable transition manifests itself as an observation. Still, looking at the way humans observe reality, typically by associating a collection of events with a single, abstract perception, the state-of-the-art notion of DES observability appears somewhat narrow. This paper presents, a generalized notion of observability, where an observation is abstract rather than concrete since it is associated with a DES behavioral scenario rather than a single component transition. To support the online diagnosis engine, knowledge compilation is performed offline. The outcome is a set of data structures, called watchers, which allow for the tracking of abstract observations.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (grant number 61972360).

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Authors and Affiliations

  1. Department of Information Engineering, University of Brescia, Brescia, Italy

    Gianfranco Lamperti & Marina Zanella

  2. School of Computer and Control Engineering, Yantai University, Yantai, China

    Xiangfu Zhao

Authors
  1. Gianfranco Lamperti
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  2. Marina Zanella
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  3. Xiangfu Zhao
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Correspondence to Gianfranco Lamperti .

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Editors and Affiliations

  1. Gdynia Maritime University, Gdynia, Poland

    Ireneusz Czarnowski

  2. Bournemouth University, Poole, UK

    Robert J. Howlett

  3. KES International, Shoreham-by-Sea, UK

    Lakhmi C. Jain

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Lamperti, G., Zanella, M., Zhao, X. (2021). Diagnosis of Active Systems with Abstract Observability. In: Czarnowski, I., Howlett, R.J., Jain, L.C. (eds) Intelligent Decision Technologies. Smart Innovation, Systems and Technologies, vol 238. Springer, Singapore. https://doi.org/10.1007/978-981-16-2765-1_42

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