Diagnosis of DES With Petri Net Models

Lefebvre, Dimitri; Delherm, C.

doi:10.1109/tase.2006.872122

Cited by 99 publications

(51 citation statements)

References 8 publications

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“…Recently, PNs have also been used for fault diagnosis applications including electro-mechanical equipment [6], power system [7], discrete event systems [3,[8][9][10][11] etc. Ushio et al have developed a Petri net model for discrete event system with faulty behaviors [8].…”

Section: Literature Reviewmentioning

confidence: 99%

A realtime fuzzy Petri net diagnoser for detecting progressive faults in PLC based discrete manufacturing system

Hsieh

2011

Int J Adv Manuf Technol

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In this paper, we explored a realtime fuzzy Petri net approach to diagnose progressive faults in discrete manufacturing systems. Progressive faults are usually caused by deterioration or aging and show stochastic properties. Some researchers have reported how to detect abrupt faults in discrete manufacturing systems using Petri net. However, little research has been conducted on Petri net diagnoser to progressive faults in discrete manufacturing event systems. To tackle this problem, we explored an approach including a realtime Petri net model and a fuzzy Petri net diagnoser to replicate the plant and detect faults in discrete manufacturing systems. The realtime Petri net model monitors events generated from the discrete manufacturing system, also compares the outputs and pre-settings. Once a difference is detected, it will start the fuzzy Petri net diagnoser to locate faults. For the purpose of validation, this approach was implemented with Visual Basic for diagnosing a dual robot arm. Evaluation experiments validated the diagnoser's performance on accuracy and diagnosability. It illustrated that the proposed approach can have a high accuracy rate of 93% and maximum diagnosis delay of eight steps; it proves that the approach has the capability of integrating knowledge and handling uncertainties. It also remedies the nonsynchronization between the diagnoser and the plant. The approach to construct the model and diagnoser is systematic; it has an excellent projection on intermittent fault diagnosis and hybrid systems.

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Section: Literature Reviewmentioning

confidence: 99%

A realtime fuzzy Petri net diagnoser for detecting progressive faults in PLC based discrete manufacturing system

Hsieh

2011

Int J Adv Manuf Technol

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“…Various approaches have been proposed with PN extensions to detect and isolate such unexpected events. These approaches are based either on the analysis of the PN reachability graph [5][6][7][8][9], on the direct properties of the PNs [10,11], or on PN unfolding [12,13]. A few results also concern the introduction of temporal information in the diagnosis process.…”

Section: Introductionmentioning

confidence: 99%

Detection of Temporal Anomalies for Partially Observed Timed PNs

Lefebvre

2017

Mathematical Problems in Engineering

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This article concerns faults detection and isolation for timed stochastic discrete event systems modeled with partially observed timed Petri nets. Events occur according to arbitrary probability density functions. The models include the sensors used to measure events and markings and also the temporal constraints to be satisfied by the system operations. These temporal constraints are defined according to tolerance intervals specified for each transition. A fault is an operation that ends too early or too late. The set of trajectories consistent with a given timed measured trajectory is first computed. Then, the probability that the temporal specifications are unsatisfied is estimated for any sequence of measurements and the probability that a temporal fault has occurred is obtained as a consequence.

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“…The general principle of our approach is the transposition in Petri Nets of the classical principle used in fault detection of continuous-variable models (Parity Space [9], Observers [15], Identification [26]). Remember that a large class of fault detection approaches relies on the different types of continuous-variable models while another class considers Discrete-Event Systems such as Petri Nets [17] [1] [12] [16] and Automata [22] [14] [23]. In this paper, changes (or faults) are considered as variations of dynamic models compared to a Petri Net which only describes the normal behavior.…”

Section: Introductionmentioning

confidence: 99%

State estimation and detection of changes in time interval models

Declerck

Guezzi

2012

Discrete Event Dyn Syst

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This paper presents an estimation approach for Time Event Graphs such as P-Time Event Graphs and Time Stream Event Graphs. It is assumed that the nominal behavior is known and that transitions are partitioned as observable and unobservable transitions. The technique is applied to the detection of changes which are (possibly small) finite variations of dynamic models compared to this nominal behavior. The detected changes provide indications that can be used in future maintenance operations.Using the algebra of dioids, the approach uses a receding-horizon estimation of the greatest state and analyzes the consistency of the data.

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Diagnosis of DES With Petri Net Models

Cited by 99 publications

References 8 publications

A realtime fuzzy Petri net diagnoser for detecting progressive faults in PLC based discrete manufacturing system

A realtime fuzzy Petri net diagnoser for detecting progressive faults in PLC based discrete manufacturing system

Detection of Temporal Anomalies for Partially Observed Timed PNs

State estimation and detection of changes in time interval models

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