Model-Based Monitoring of a Train Passenger Access System (original) (raw)
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Diagnosability of Discrete Event Systems: a Petri Net based Approach
2004
This work deals with model based fault diagnosis of discrete event systems. The model of the system, expressed as an interpreted Petri net (IPN) describes partially observed events and states, and includes all possible faulty states. Based on a modular modelling methodology, the input-output diagnosability property is introduced and structurally characterized. Then a diagnoser scheme is proposed allowing fault detection and location in polynomial time.
Fault Diagnosis in Discrete Event Systems Using Interpreted Petri Nets
Advances in Robotics, Automation and Control, 2008
Diagnosability property and fault detection schemes have been widely addressed on centralized approaches using the global model of the Discrete Event System (DES). Roughly speaking, diagnosability is the property of determining if using the system model is possible to detect and locate the faulty states in a finite number of steps. In the works (Sampath, et al., 1995) and (Sampath, et al., 1996), a method for modeling a DES using finite automata is proposed; based on this model, a diagnoser is derived. The cycles in the diagnoser are used to determine when the DES is diagnosable. Recently, fault diagnosis of DES has been addressed through a distributed approach allowing breaking down the complexity when dealing with large and complex systems
IFAC-PapersOnLine, 2022
This paper is concerned with an online model-based fault diagnosis of discrete event systems. The model of the system is built using the interpreted Petri nets (IPN) formalism. The model includes the normal system states as well as all possible faulty states. Moreover, it assumes the general case when events and states are partially observed. One of the contributions of this work is a bottom-up modeling methodology. It describes the behavior of system elements using the required states variables and assigning a range to each state variable. Then, each state variable is represented by an IPN model, herein named module. Afterwards, using two composition operators over all the modules, a monolithic model for the whole system is derived. It is a very general modeling methodology that avoids tuning phases and the state combinatory found in finite state automata (FSA) approaches. Another contribution is a definition of diagnosability for IPN models built with the above methodology and a structural characterization of this property; polynomial algorithms for checking diagnosability of IPN are proposed, avoiding the reachability analysis of other approaches. The last contribution is a scheme for online diagnosis; it is based on the IPN model of the system and an efficient algorithm to detect and locate the faulty state. Note to Practitioners-The results proposed in this paper allow: 1) building discrete event system models in which faults may arise; 2) testing the diagnosability of the model; and 3) implementing an online diagnoser. The modeling methodology helps to conceive in a natural way the model from the description of the system's components leading to modules that are easily interconnected. The diagnosability test is stated as a linear programming problem which can be straightforward programmed. Finally, the algorithm for online diagnosis leads to an efficient procedure that monitors the system's outputs and handles the normal behavior model. This provides an opportune detection and location of faults occurring within the system. Index Terms-Diagnosability of discrete event systems, modelbased fault diagnosis, modeling methods based on interpreted Petri nets (PN).
Fault Diagnosis Using Petri Nets: a case study
2012
The motivation of this work is to ensure that a Discrete Event System continues working after a fault occurs, since some systems, like power plants, should not stop working because they can generate service interruptions that cause severe economic impacts and even dangers. So, the interest of this paper is to analyze the Discrete Event System model to detect faults using Interpreted Petri nets and here is presents a case study about how can be uses a diagnosis scheme in order to identify if there exists a fault, considering permanent and control faults that are modeled with Petri nets.
Online Fault Diagnosis of Discrete Event Systems. A Petri Net-Based Approach
IEEE Transactions Automation Science and Engineering, 2007
This paper is concerned with an online model-based fault diagnosis of discrete event systems. The model of the system is built using the interpreted Petri nets (IPN) formalism. The model includes the normal system states as well as all possible faulty states. Moreover, it assumes the general case when events and states are partially observed. One of the contributions of this work is a bottom-up modeling methodology. It describes the behavior of system elements using the required states variables and assigning a range to each state variable. Then, each state variable is represented by an IPN model, herein named module. Afterwards, using two composition operators over all the modules, a monolithic model for the whole system is derived. It is a very general modeling methodology that avoids tuning phases and the state combinatory found in finite state automata (FSA) approaches. Another contribution is a definition of diagnosability for IPN models built with the above methodology and a structural characterization of this property; polynomial algorithms for checking diagnosability of IPN are proposed, avoiding the reachability analysis of other approaches. The last contribution is a scheme for online diagnosis; it is based on the IPN model of the system and an efficient algorithm to detect and locate the faulty state.
Failure diagnosis: a case study on modeling and analysis by Petri nets
SMC'03 Conference Proceedings. 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme - System Security and Assurance (Cat. No.03CH37483), 2003
Failure diagnosis in the confexf of DES was first formulated by Sampath et al. in [5] [4], where the notion of diagnosability and the associated diagnoser are proposed. Ushio et al. [ 6 ] extended Sumpath's study to systems modeled by Petri nets. This paper further assumes some of the transitions in a Petri net model are observable in the sense that its occurrence con be observed. The main contribution of this study shows how diagnosers and v e r i j k s for systems modeled by Petri net are consfructed accordingly. As shown by examples, the additional information from observed transitions adds diagnosability io the system.
This work is related to On-the-Fly PEtri-Net-based Diagnosability Analyser (OF-PENDA), a software tool to assess diagnosability of discrete event systems (DESs) modeled by labeled Petri nets (LPNs). OF-PENDA, aiming at coping with state explosion problem, is the implementation of the diagnosability analysis techniques developed in [1]. Three aspects for OF-PENDA are discussed in this paper: the on-the-fly and incremental techniques for diagnosability analysis; the features of OF-PENDA tool; and two illustrative cases, the WODES and the level crossing (LC) benchmarks, to show the efficiency in terms of time and memory compared with some existing approaches.
Integrated fault diagnosis based on Petri net models
2007
Abstract This paper extends an existing sensor mapping procedure, defines compatibility of models and proposes an integrated methodology based on existing methodologies for the construction of diagnosers for discrete event systems modeled by Petri Nets. An industrial application is used as a case study to illustrate the theoretical results of the paper.
Failure components detection in discrete event systems modeled by Petri net
3rd International Conference on Systems and Control, 2013
This paper addresses the problem of fault diagnosis in discrete event systems (DES) modeled by Petri nets with outputs, i.e., Petri net (PN) with place sensors and transition sensors. The PN is used in order to give a functional and dysfunctional modeling of the system studied. In the dysfunctional modeling, places represent the failure mode and transitions represent the conditions that led a system to a failure mode. These Places and transitions are modeled as unobservable places and transitions. Therefore, a new method based on an Unknown Input Observer (UIO) is proposed to detect the failure mode functioning as well as the instants of the occurrence of these faults in DES modeled by PN. The sufficient conditions for the existence of the observer are also given. Finally, a simple photovoltaic system will be considered as an example to show the effectiveness of the proposed approach.
Evaluating Fault Tree by means of Colored Petri nets to analyze the railway system dependability
Safety Science, 2018
Railway system is a safety critical and time-related system, the system's states and time parameters can be used to carry out the dependability and hazard analysis. Fault Tree is widely recognized as a standard evaluating method. However, restricted by the commercial products, the Fault Tree is limited to assess dynamic systems with event-repair operations and time-related attributions. Additionally, it is difficult to incorporate non-linear relationships such as feedback. The quality assurance for fault trees and events trees is mainly carried out by peer review. Combinatory limitations are encountered when modeling complex events with classical methods. Thus, this paper proposes a new method to represent and extend the Fault Tree in Colored Petri nets. Due to large calculation capabilities of CPNs, these limitations can be able to overcome. Additionally, it can be reused for customizations. The accuracy of the approach is verified by using model-based simulation and state space analysis. The performance and benefits of the new approach are demonstrated by investigating train to train collision failure models. To increase the safety demanding needs of railway transportation, we propose a new train movement authority plus system (MA+) in this paper. With the assistance of the wireless communication technology, MA+ can detect the condition of approaching switches and encountering trains within a certain range. The results indicate that the new MA+ can reduce the risk of train head to tail collisions. What is more, the new evaluation method can offer much more essential information, which involves maintenance components, model correctness verification, time factors, and mathematical calculation together, than the traditional Fault Tree Analysis.