Distributed Diagnosis of Place-Bordered Petri Nets

Abstract

Abstract-This paper studies on-line fault detection and isolation of modular dynamic systems modeled as sets of place-bordered Petri nets. The common places among the set of Petri nets modeling a system capture coupling of various system components. The transitions are labeled by events, some of which are unobservable, i.e., not directly recorded by the sensors attached to the system. The events whose occurrence must be diagnosed have unobservable transition labels. These events model faults or other significant changes in the system state. The existing theory of diagnosis of discrete-event systems is extended in the context of the above model. The modular structure of the system is exploited by a distributed algorithm for fault diagnosis. A Petri net diagnoser is associated to every Petri net and the diagnosers communicate in real-time during the diagnostic process when the token count of common places changes. A merge function is defined to combine the individual diagnoser states and recover the complete diagnoser state that would be obtained under a monolithic approach. Strategies that reduce the communication overhead are presented. The software implementation of the distributed algorithm is discussed.Note to practitioner -In the last decade monitoring, fault detection, and diagnosis methodologies based on the use of discrete-event models have been successfully used in a variety of technological systems ranging from document processing systems to intelligent transportation systems. This paper was motivated by the problem of fault diagnosis for modular (distributed) dynamic discreteevent systems (DES). As a DES modeling formalism, Petri nets offer potential advantages in terms of the distributed representation of the system and of the ability to represent coupling of the system components. The systems studied in this paper are sets of modules coupled with each other through various system components and modeled using Petri nets. We present a distributed fault diagnosis algorithm which allows each module in the distributed system to diagnose its faults independently unless completion of a task requires the use of coupled components.

Fault detection for discrete event systems using Petri nets with unobservable transitions

“…9 , p 10 , p 11 ). In the two lines parts are processed, namely smoothed, cleaned up, painted and polished (this corresponds to the firing of transitions ε 3 to ε 10 ).…”

“…Among the first pioneering works dealing with Petri nets (PNs), we recall the approach of Prock that proposes an on-line technique for fault detection that is based on monitoring the number of tokens residing into P-invariants [13]. In [9] Genc and Lafortune propose a diagnoser on the basis of a modular approach that performs the diagnosis of faults in each module. In [2] Benveniste et al present a net unfolding approach for designing an on-line asynchronous diagnoser.…”

On fault diagnosis of random free-choice Petri nets

“…Deterministic and stochastic finiteautomata models are used in Thorsley and Theneketzis [2005], Wang et al [2004], deterministic Petri nets e.g. in Benveniste et al [2003], , Boel and Jiroveanu [2004], Giua and Seatzu [2005], Jiroveanu [2006], Genc and Lafortune [2007], Jiroveanu et al [2008], partially This paper presents research results of the Belgian Network DYSCO, funded by the IAP Programme, initiated by the Belgian State, Science Policy Office. The work of the first author was supported by the VEGA grants 1/0649/09, VG 1/0822/08 of the Slovak Grant Agency.…”

Las Redes de Petri Interpretadas en el Diagnóstico de Fallos de Sistemas Híbridos. Aplicación a un Helicóptero no Tripulado

“…Porúltimo está la investigación de [35], enél realiza diagnóstico de fallos utilizando RdP con lugares limitados, utiliza el mismo ejemplo de HVAC de [88], en este trabajo modela los componentes sin hacer composición sincrónica, hace un algoritmo de diagnóstico distribuido donde identifica los fallos de manera independiente. El diagnóstico es simulado inicialmente con Matlab y después es integrado con la herramienta Graphviz de AT&T. Esta técnica es compleja de implementar y poco posible de aplicar a procesos industriales con mediano grado de complejidad.…”