Diagnosability Of Petri Nets Research Articles

Fault Diagnosis for Discrete Event Systems Modeled By Bounded Petri Nets

AbstractFault diagnosis is an important problem in the manufacturing industry. It has been extensively studied in the past few decades both in time‐driven systems and discrete event systems. This paper presents a Petri net diagnoser for online fault diagnosis of discrete event systems modeled by bounded labeled Petri nets. First, we present the concept and some properties of an extended basis reachability graph. Next, based on such a graph, we construct a Petri net diagnoser that is used to determine if a fault has occurred. Finally, an example is given to illustrate the application of the proposed diagnoser.

Diagnosability of Petri nets with observation graphs

This paper addresses the problem of diagnosability for dynamic discrete event systems modeled with bounded or unbounded Petri nets that are deadlock-free and monitored with sensor configurations with marking and event measurements. The proposed method gives necessary and sufficient conditions for diagnosability. It is based on the transformation of the coverability graph into an observation graph that encodes all observation sequences of measured markings and events with respect to the sensor configuration. This graph also encodes all sequences of transitions that may fire from any reachable marking of the Petri net. Diagnosability is determined by analyzing the paths and circuits in the observation graph. The method is illustrated with several examples of bounded or unbounded Petri nets.

A Petri Net Diagnoser for Discrete Event Systems Modeled by Finite State Automata

We propose in this paper a Petri net approach to online diagnosis of discrete event systems (DESs) modeled by finite state automata. The diagnosis method is based on the construction of a Petri net diagnoser (PND) which is constructed in polynomial time and requires less memory than other methods proposed in the literature. We also present methods for the conversion of the PND to both sequential function chart and ladder diagram for implementation on a programmable logic controller (PLC). Implementation issues are also addressed in the paper.

Systematic Process for Building a Fault Diagnoser Based on Petri Nets Applied to a Helicopter

This work presents a systematic process for building a Fault Diagnoser (FD), based on Petri Nets (PNs) which has been applied to a small helicopter. This novel tool is able to detect both intermittent and permanent faults. The work carried out is discussed from theoretical and practical point of view. The procedure begins with a division of the whole system into subsystems, which are the devices that have to be modeled by using PN, considering both the normal and fault operations. Subsequently, the models are integrated into a global Petri Net diagnoser (PND) that is able to monitor a whole helicopter and show critical variables to the operator in order to determine the UAV health, preventing accidents in this manner. A Data Acquisition System (DAQ) has been designed for collecting data during the flights and feeding PN diagnoser with them. Several real flights (nominal or under failure) have been carried out to perform the diagnoser setup and verify its performance. A summary of the validation results obtained during real flight tests is also included. An extensive use of this tool will improve preventive maintenance protocols for UAVs (especially helicopters) and allow establishing recommendations in regulations.

A Distributed Fault Detection Approach for Industrial Systems

Abstract The paper addresses the fault detection problem for large discrete event systems that can be modelled by a set of interacting Petri Net (PN) modules. Each system module is monitored by a PN diagnoser that has local information on the module structure and shares information by some places that are coupled with the other modules of the system. Each diagnoser works on-line: it waits for the firing of an observable transition and employs an algorithm based on the definition of some integer linear programming problems to decide whether the system behaviour is normal or exhibits some possible faults. A case study presented in the related literature shows the distributed fault detection strategy and points out the efficiency of the proposed approach.

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

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.

Distributed Diagnosis of Place-Bordered Petri Nets

This paper studies online 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 with 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 Practitioners-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 discrete-event systems (DES). As a DES modeling formalism, Petri nets offer potential advantages in terms of the distributed representation of the system and 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. In the case of coupling, modules communicate with each other to accurately diagnose the fault. The distributed fault diagnosis algorithm recovers the monolithic diagnosis information at the cost of communication and growing communication overhead. To mitigate that problem, we present an improved version of the algorithm that significantly reduces the communication overhead. Finally, we introduce the software toolbox (written in Matlab and integrated with AT&T Graphviz) and we present a case study of an example of a heating, ventilation, and air-conditioning system where we use the software tool for modeling and analyzing the system