Online Fault Diagnosis of Discrete Event Systems. A Petri Net-Based Approach

Ramírez-Treviño, A.; Ruiz-Beltrán, E.; Rivera-Rangel, I.; López-Mellado, Ernesto

doi:10.1109/tase.2006.872120

Cited by 151 publications

(57 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…An interpreted PN is input -output diagnosable in r steps if any marking M resulting immediately from the firing of a fault transition is distinguishable from any other marking M' by firing any sequence with r transitions (Alcaraz-Mejia et al, 2003;Ramirez-Trevino et al, 2004). Several structural characterizations of input -output diagnosability have been provided: necessary and sufficient conditions related to inputoutput relationships between places, sufficient conditions when the normal behaviour of the interpreted PN is event detectable (Alcaraz-Mejia et al, 2003;Ramirez-Trevino et al, 2004;Ramirez-Trevino et al, 2007). In order to illustrate event diagnosability, let us consider again PN2 in figure 2.…”

Section: Event Detectabilitymentioning

confidence: 99%

Diagnosis of Discrete Event Systems with Petri Nets

Lefebvre¹

2008

Petri Net, Theory and Applications

View full text Add to dashboard Cite

Section: Event Detectabilitymentioning

confidence: 99%

Diagnosis of Discrete Event Systems with Petri Nets

Lefebvre¹

2008

Petri Net, Theory and Applications

View full text Add to dashboard Cite

“…Others are associated with the validation of DES, which includes: diagnosis of an asynchronous system [11]; detecting and isolating fault events [12]; fault online detection [13]; fault diagnosis with unobservable transition [14]; model checking based on user specification [15]; formal verification with the use of structural reasoning and general unary hypothesis automaton (GUHA) methods [16]. Other research concerns the application of PNs to specific problems, e.g., to model flexible manufacturing systems (FMS) [17][18][19][20][21][22] or to model As a continuation of [53], which lacked a formal description of the proposed solution, this article may be included in the second trend of dealing with DES.…”

Section: Introductionmentioning

confidence: 99%

A Program Model of Fuzzy Interpreted Petri Net to Control Discrete Event Systems

Markiewicz

Gniewek

2017

Applied Sciences

View full text Add to dashboard Cite

Using Petri nets (PNs) to control discrete event systems (DES) has many benefits, because of their graphical representations, the possibility of parallel process control, and their formal descriptions. Amongst the different PNs that are applied for this purpose, most have some limitations for visualization. For many of these PNs, another restriction is the length of time between the creation of the control algorithm in the form of a graph and its practical implementation. These two issues can be resolved with one solution called fuzzy interpreted PN (FIPN). This article proposes the use of a program model based on FIPN to control DES and the method for generation of this model using the graphical representation of the net. FIPN offers a better visualization in comparison to discrete PNs and it allows for the quick creation of program code through the application of a simulator called FIPN-SML. This computer tool implements a method that transforms the graphical form of FIPN into Structured Text (ST) language supported by the IEC 61131-3.

show abstract

“…r 3 Speed of Conveyor 3 is reduced. r 4 Speed of Conveyor 4 is reduced. r 5 Speed of Crane is reduced.…”

mentioning

confidence: 99%

Probabilistic Fault Diagnosis of Event-driven System Focused on Sequential Order of Processes

Yamaguchi¹,

Inagaki²,

Suzuki³

2014

Transactions of the Society of Instrument and Control Engineers

View full text Add to dashboard Cite

This paper presents a fault diagnose method for event-driven systems based on probabilistic inference, Timed Markov Model (TMM). The TMM is one of semi-Markov process models wherein state transition probabilities depend on stay time at a state. The TMM requires numerous amount of learning data to establish a diagnosis system, and it is difficult to collect enough learning data in a large and complex system. This paper focuses on a diagnosis strategy based on sequential order of processes in the event-driven system, and the method can diagnose the fault even if the amount of learning data is not rich. Finally, the usefulness of the proposed strategy is verified through some experimental results of an automatic transfer line.Key Words: event-driven system, fault diagnosis, Timed Markov Model, sequential order of processes 1.

show abstract

Online Fault Diagnosis of Discrete Event Systems. A Petri Net-Based Approach

Cited by 151 publications

References 19 publications

Diagnosis of Discrete Event Systems with Petri Nets

Diagnosis of Discrete Event Systems with Petri Nets

A Program Model of Fuzzy Interpreted Petri Net to Control Discrete Event Systems

Probabilistic Fault Diagnosis of Event-driven System Focused on Sequential Order of Processes

Contact Info

Product

Resources

About