Diagnosis of Discrete Event Systems with Petri Nets

Lefebvre, Dimitri

doi:10.5772/5325

Cited by 9 publications

(2 citation statements)

References 29 publications

(27 reference statements)

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“…When only the event sequence is observed, directed paths in the PN graph [17] can be used to recover the wrong or missing events. When the event and state sequences are both observed, estimation methods based for example on coding theory [18] will be combined with the identification algorithm.…”

Section: Discussionmentioning

confidence: 99%

“…OK (0 0 0 0 0 1 1 0 0 0) T s 10 OK (0 0 0 1 0 1 0 0 0 0) T s 11 OK (0 0 0 1 0 0 0 1 0 0) T s 12 OK (1 0 0 0 0 1 0 0 0 0) T s 13 OK (0 1 0 0 0 1 0 0 0 0) T s 14 OK (0 0 0 0 0 0 1 1 0 0) T s 15 OK (0 0 0 0 1 1 0 0 0 0) T s 16 Fault on line 2 (0 0 0 0 0 0 0 1 0 1) T s 17 Fault on line 1 (0 0 0 1 0 0 0 0 1 0) T s 18 Fault on line 1 (0 0 0 0 1 0 0 0 1 0) T s 19 Fault on line 2 (0 0 0 0 0 1 0 0 0 1) T s 20 Fault on line 2 (0 0 1 0 0 0 0 0 0 1) T s 21 Fault on line 1 (0 0 0 0 0 0 1 0 1 0) T s 22 Fault on line 1 (1 0 0 0 0 0 0 0 1 0) T s 23 Fault on line 1 (0 1 0 0 0 0 0 0 1 0) T s 24 Faults on lines 1 and 2 (0 0 0 0 0 0 0 0 1 1) T system is not compatible because rank (A) ¼ 12 (after a transitory that corresponds to the first 1000 TU) and rank (A | b) ¼ 13, but an approximated solution is obtained according to the pseudo inverse of matrix A. Estimated values of transition parameters are given in Table 3.…”

Section: Spn Identificationmentioning

confidence: 99%

See 1 more Smart Citation

Identification of stochastic and deterministic stochastic Petri net models for reliability analysis

Medhi

Leclercq

Greah

2008

Proceedings of the Institution of Mechanical Engineers, Part O:

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Reliability analysis for dynamical systems is often based on timed stochastic Petri net (PN) models. A priori knowledge about failure processes is difficult to obtain and, as a consequence, the model structure and parameters are mainly unknown. In that case, synthesis and identification methods based on analysis of collected event sequences are of great interest. The contribution of this paper concerns the identification of timed stochastic PN models. Stochastic and deterministic stochastic PNs with deterministic and exponentially distributed transition durations are considered. A systematic identification method is proposed according to event sequences that are recorded by supervision systems. This method is based on the idea that the dynamic behaviour of considered PN can be mapped into a Markov model with state space isomorphic to the reachability graph of the untimed PN model.

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Section: Discussionmentioning

confidence: 99%