Sensor selection for observability in Interpreted Petri Nets: a genetic approach

Aguirre-Salas, L.

doi:10.1109/cdc.2003.1271734

Cited by 18 publications

(12 citation statements)

References 7 publications

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“…In the previous example, p 1 is sufficient to distinguish transitions associated with b, but is not sufficient to distinguish transitions associated with ε. Now we define the scoring function for a measurable place 46th IEEE CDC, New Orleans, USA, Dec. [12][13][14]2007 WePI24.17…”

Section: Examplementioning

confidence: 99%

“…In contrast, there is limited previous work on the sensor selection problem when the underlying model is a Petri net. In [11], [12], observability notions are used as criteria when optimizing the selection of sensors in interpreted Petri net (IPN) models [13]. In [12], genetic algorithms are used to approximate the optimal sensor selection, but the main disadvantage is that they converge very slowly to a suboptimal solution.…”

Section: Introductionmentioning

confidence: 99%

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Approximating optimal place sensor selection for structural observability in discrete event systems modeled by Petri Nets

Hadjicostis

2007

2007 46th IEEE Conference on Decision and Control

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In this paper, we study place sensor selection problems in Discrete Event Systems modeled by Petri nets with outputs (i.e., Petri nets with transition and place sensors). Our goal is to place a minimal number of sensors so as to maintain the property of structural observability, i.e., the ability to uniquely determine the system state based on sensor information (and knowledge of the system model and initial state). The place sensor selection problem has been shown to be N P-complete and this paper proposes two efficient algorithms to approximate its solution. Simulations suggest that the two methods run faster and can find reasonably good solutions compared to methods that are based on linear integer programming solvers.

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Section: Examplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Approximating optimal place sensor selection for structural observability in discrete event systems modeled by Petri Nets

Hadjicostis

2007

2007 46th IEEE Conference on Decision and Control

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“…We develop necessary and sufficient conditions for residual generator selection in a hybrid automaton so that a failure mode becomes diagnosable in the extended DES model of the hybrid system and residual generators. The problem of residual generator selection studied for hybrid automata can be considered as the counterpart of the sensor selection problem discussed in [1] and [7] for purely DES.…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of hybrid systems: Part 2- residual generator selection and diagnosis in the presence of unreliable residual generators

Mohammadi

Hashtrudi-Zad

Khorasani

2009

2009 IEEE International Conference on Systems, Man and Cybernetics

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We have presented a framework in [6] for fault diagnosis of hybrid systems modeled by hybrid automata and investigated the diagnosability of failure modes in hybrid automata. In this framework, we assume that there is a bank of residual generators designed for the continuous dynamics of the system. We have developed a hybrid diagnosis approach in which faults are diagnosed by integrating the information generated by the residual generators and the information at the discreteevent system representation of the system. In this paper, we study the problem of residual generator selection in hybrid automata. Moreover, we investigate fault diagnosis of hybrid systems in the presence of unreliable residual generators generating false alarms or false silence signals.

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“…In the literature there is a number of results about optimal static sensors selection in the context of finite state automata [8], [7], but only few results are available in the context of Petri nets (PNs), where the optimal static sensors selection problem has been essentially considered to make observable or structural observable a net system [10], [1], but not to ensure diagnosability.…”

Section: Introductionmentioning

confidence: 99%

Sensors selection for K-diagnosability of Petri nets via Integer Linear Programming

Basile

Tommasi

Sterle

2015

2015 23rd Mediterranean Conference on Control and Automation (MED)

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This paper studies the problem of static sensor selection for ensuring K-diagnosability in bounded Petri nets. An integer linear programming problem is formulated to determine the minimal number of randomly selected sensors that make K-diagnosable net system with respect to a fault. This value is an estimate of the minimum number of sensors that assures the K-diagnosability of a given fault, which can be further improved taking into account some elements of the net structures

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Sensor selection for observability in Interpreted Petri Nets: a genetic approach

Cited by 18 publications

References 7 publications

Approximating optimal place sensor selection for structural observability in discrete event systems modeled by Petri Nets

Approximating optimal place sensor selection for structural observability in discrete event systems modeled by Petri Nets

Diagnosis of hybrid systems: Part 2- residual generator selection and diagnosis in the presence of unreliable residual generators

Sensors selection for K-diagnosability of Petri nets via Integer Linear Programming

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