Design and Identification of Stochastic and Deterministic Stochastic Petri Nets

Mehdi, Souleiman Ould El; Bekrar, Rebiha; Messai, Nadhir; Leclercq, Edouard; Lefebvre, Dimitri; Riera, Bernard

doi:10.1109/tsmca.2011.2173798

Cited by 19 publications

(5 citation statements)

References 26 publications

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“…In (Klein, 2005), (Roth, 2009(Roth, , 2010 are considered both inputs and outputs, but cyclic sequences are supposed to be known. Techniques in (Giua 2005), (Cabasino, 2007), (Dotoli, 2011), (Cook, 2004)(van der Aalst, 2004, (Ould El Medhi, 2006, 2012 process as input data a language generated by the system, which is given as events sequences, regardless how the events are obtained from I/O data. Regarding our previous works (Estrada 2009), (Estrada 2010), the assumption about cyclic sequences as input data is dropped off in the current proposal, allowing addressing actual controlled DES.…”

Section: Discussionmentioning

confidence: 99%

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A stepwise method for identification of controlled discrete manufacturing systems

Estrada-Vargas¹,

Lesage²,

López-Mellado³

2014

International Journal of Computer Integrated Manufacturing

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This paper deals with the identification of discrete event manufacturing systems that are automated by a programmable logic controller (PLC). The behaviour of the closed loop system (PLC and Plant) is observed during its operation and is represented by a single long sequence of observed input/output signals vectors. The proposed method follows a black-box and passive identification approach; it allows building stepwise an interpreted Petri net (IPN) model. The identification method is composed of several polynomial time algorithms implemented in a software tool that generates and draws the IPN model.

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Section: Discussionmentioning

confidence: 99%

“…Other works pursue the construction of a stochastic PN from recorded event sequences (Ould El Medhi, 2006, 2012 for reliability applications.…”

Section: Related Workmentioning

confidence: 99%

A stepwise method for identification of controlled discrete manufacturing systems

Estrada-Vargas¹,

Lesage²,

López-Mellado³

2014

International Journal of Computer Integrated Manufacturing

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“…Process mining [8], [9], and advanced system identification methods [10], [11] provide semi-automated means to produce process and system models for various purposes including process conformance (i.e., determining whether or not the actual process being practised conforms to the normative process). Typically, these methods have the goal of capturing the most frequent process patterns and exhibiting robustness to noise.…”

Section: Related Workmentioning

confidence: 99%

“…Some relevant work associates more closely with system identification than process mining. Several of these, including [11], [10] and [14] use integer linear programming (ILP). Ould El Mehdi[11] et al uses ILP to produce deterministic and stochastic Petri net (DSPN) models of systems.…”

Section: Related Workmentioning

confidence: 99%

Dynamic production system identification for smart manufacturing systems

Denno

Dickerson

Harding

2018

Journal of Manufacturing Systems

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This paper presents a methodology, called production system identification, to produce a model of a manufacturing system from logs of the system’s operation. The model produced is intended to aid in making production scheduling decisions. Production system identification is similar to machine-learning methods of process mining in that they both use logs of operations. However, process mining falls short of addressing important requirements; process mining does not (1) account for infrequent exceptional events that may provide insight into system capabilities and reliability, (2) offer means to validate the model relative to an understanding of causes, and (3) updated the model as the situation on the production floor changes. The paper describes a genetic programming (GP) methodology that uses Petri nets, probabilistic neural nets, and a causal model of production system dynamics to address these shortcomings. A coloured Petri net formalism appropriate to GP is developed and used to interpret the log. Interpreted logs provide a relation between Petri net states and exceptional system states that can be learned by means of novel formulation of probabilistic neural nets (PNNs). A generalized stochastic Petri net and the PNNs are used to validate the GP-generated solutions. The methodology is evaluated with an example based on an automotive assembly system.

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“…This approach is being explored for other PN classes [10] [11]. A recent stochastic approach allows obtaining timed PN models [12]. Other related works can be found in surveys on identification methods in [13] and [14].…”

Section: Introductionmentioning

confidence: 99%

Identification of partially observable discrete event manufacturing systems

Estrada-Vargas

López-Mellado

Lesage

2013

2013 IEEE 18th Conference on Emerging Technologies &Amp; Factory Automation (ETFA)

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This paper deals with black-box identification of discrete event manufacturing systems that are automated using a programmable logic controller (PLC). The behavior of the system is observed during its operation and is represented by a single long sequence w of observed input/output (I/O) signals vectors. The identification method, conceived for addressing large and complex industrial systems, consists of two complementary stages; the first one obtains, from w, the observable part of an interpreted Petri net (PN) model composed of observable places and transitions describing the reactive behavior of the system. Afterwards, w is transformed into a sequence S of transition firings from which a PN model that reproduces S is inferred. This paper focuses on the second stage of the method in which a PN is built by adding non-labeled places and arcs that represent the non-observed behavior of the whole system by assuring the reproduction of w; this technique is based on discovering the causal and concurrent relationships between transitions in S.

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Design and Identification of Stochastic and Deterministic Stochastic Petri Nets

Cited by 19 publications

References 26 publications

A stepwise method for identification of controlled discrete manufacturing systems

A stepwise method for identification of controlled discrete manufacturing systems

Dynamic production system identification for smart manufacturing systems

Identification of partially observable discrete event manufacturing systems

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