Abstract
Model-based diagnosis was first proposed for static systems, where the values of the input and output variables are given at a single time point and the root cause of an observed misbehavior is a set of faults. This set-oriented perspective of the diagnosis results was later adopted also for dynamical systems, although it fits neither the temporal nature of their observations, which are gathered over a time interval, nor the temporal evolution of their behavior. This conceptual mismatch is bound to make diagnosis of discrete-event systems (DESs) poor in explainability. Embedding the reciprocal temporal ordering of faults in diagnosis results may be essential for critical decision-making. To favor explainability, the notions of temporal fault, explanation, and explainer are introduced in diagnosis during monitoring of DESs. To achieve explanatory monitoring, a technique is described, which progressively refines the diagnosis results produced already.
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Notes
- 1.
A regular expression is defined inductively over an alphabet \(\varSigma \) as follows. The empty symbol \({\varepsilon }\) is a regular expression. If \(a \in \varSigma \), then a is a regular expression. If x and y are regular expressions, then the followings are regular expressions: (x) (parentheses may be used), \(x {\; | \;}y\) (alternative), xy (concatenation), x? (optionality), \(x^*\) (repetition zero or more times), and \(x^+\) (repetition one or more times). When parentheses are missing, the concatenation has precedence over the alternative, while optionality and repetition have highest precedence; for example, \(a b^*{\; | \;}cd?\) denotes \((a (b)^*) {\; | \;}c(d)?\).
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Acknowledgements
This work was supported in part by Regione Lombardia (project Smart4CPPS, Linea Accordi per Ricerca, Sviluppo e Innovazione, POR-FESR 2014–2020 Asse I) and by the National Natural Science Foundation of China (grant number 61972360).
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Bertoglio, N., Lamperti, G., Zanella, M., Zhao, X. (2020). Explanatory Monitoring of Discrete-Event Systems. In: Czarnowski, I., Howlett, R., Jain, L. (eds) Intelligent Decision Technologies. IDT 2020. Smart Innovation, Systems and Technologies, vol 193. Springer, Singapore. https://doi.org/10.1007/978-981-15-5925-9_6
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