3,227 research outputs found

    Stochastic DES Fault Diagnosis with Coloured Interpreted Petri Nets

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    [EN] This proposal presents an online method to detect and isolate faults in stochastic discrete event systems without previous model. A coloured timed interpreted Petri Net generates the normal behavior language after an identification stage.The next step is fault detection that is carried out by comparing the observed event sequences with the expected event sequences. Once a new fault is detected, a learning algorithm changes the structure of the diagnoser, so it is able to learn new fault languages. Moreover, the diagnoser includes timed events to represent and diagnose stochastic languages. Finally, this paper proposes a detectability condition for stochastic DES and the sufficient and necessary conditions are proved.This work was supported by a grant from the Universidad del Cauca, Reference 2.3-31.2/05 2011.Muñoz-Añasco, DM.; Correcher Salvador, A.; García Moreno, E.; Morant Anglada, FJ. (2015). Stochastic DES Fault Diagnosis with Coloured Interpreted Petri Nets. Mathematical Problems in Engineering. 2015:1-13. https://doi.org/10.1155/2015/303107S1132015Jiang, S., & Kumar, R. (2004). Failure Diagnosis of Discrete-Event Systems With Linear-Time Temporal Logic Specifications. IEEE Transactions on Automatic Control, 49(6), 934-945. doi:10.1109/tac.2004.829616Zaytoon, J., & Lafortune, S. (2013). Overview of fault diagnosis methods for Discrete Event Systems. Annual Reviews in Control, 37(2), 308-320. doi:10.1016/j.arcontrol.2013.09.009Sampath, M., Sengupta, R., Lafortune, S., Sinnamohideen, K., & Teneketzis, D. (1995). Diagnosability of discrete-event systems. IEEE Transactions on Automatic Control, 40(9), 1555-1575. doi:10.1109/9.412626Sampath, M., Sengupta, R., Lafortune, S., Sinnamohideen, K., & Teneketzis, D. C. (1996). Failure diagnosis using discrete-event models. IEEE Transactions on Control Systems Technology, 4(2), 105-124. doi:10.1109/87.486338Estrada-Vargas, A. P., López-Mellado, E., & Lesage, J.-J. (2010). A Comparative Analysis of Recent Identification Approaches for Discrete-Event Systems. Mathematical Problems in Engineering, 2010, 1-21. doi:10.1155/2010/453254Cabasino, M. P., Giua, A., & Seatzu, C. (2010). Fault detection for discrete event systems using Petri nets with unobservable transitions. Automatica, 46(9), 1531-1539. doi:10.1016/j.automatica.2010.06.013Prock, J. (1991). A new technique for fault detection using Petri nets. Automatica, 27(2), 239-245. doi:10.1016/0005-1098(91)90074-cAghasaryan, A., Fabre, E., Benveniste, A., Boubour, R., & Jard, C. (1998). Discrete Event Dynamic Systems, 8(2), 203-231. doi:10.1023/a:1008241818642Hadjicostis, C. N., & Verghese, G. C. (1999). Monitoring Discrete Event Systems Using Petri Net Embeddings. Application and Theory of Petri Nets 1999, 188-207. doi:10.1007/3-540-48745-x_12Benveniste, A., Fabre, E., Haar, S., & Jard, C. (2003). Diagnosis of asynchronous discrete-event systems: a net unfolding approach. IEEE Transactions on Automatic Control, 48(5), 714-727. doi:10.1109/tac.2003.811249Genc, S., & Lafortune, S. (2003). Distributed Diagnosis of Discrete-Event Systems Using Petri Nets. Lecture Notes in Computer Science, 316-336. doi:10.1007/3-540-44919-1_21Genc, S., & Lafortune, S. (2007). Distributed Diagnosis of Place-Bordered Petri Nets. IEEE Transactions on Automation Science and Engineering, 4(2), 206-219. doi:10.1109/tase.2006.879916Ramirez-Trevino, A., Ruiz-Beltran, E., Rivera-Rangel, I., & Lopez-Mellado, E. (2007). Online Fault Diagnosis of Discrete Event Systems. A Petri Net-Based Approach. IEEE Transactions on Automation Science and Engineering, 4(1), 31-39. doi:10.1109/tase.2006.872120Dotoli, M., Fanti, M. P., Mangini, A. M., & Ukovich, W. (2009). On-line fault detection in discrete event systems by Petri nets and integer linear programming. Automatica, 45(11), 2665-2672. doi:10.1016/j.automatica.2009.07.021Fanti, M. P., Mangini, A. M., & Ukovich, W. (2013). Fault Detection by Labeled Petri Nets in Centralized and Distributed Approaches. IEEE Transactions on Automation Science and Engineering, 10(2), 392-404. doi:10.1109/tase.2012.2203596Basile, F., Chiacchio, P., & De Tommasi, G. (2009). An Efficient Approach for Online Diagnosis of Discrete Event Systems. IEEE Transactions on Automatic Control, 54(4), 748-759. doi:10.1109/tac.2009.2014932Roth, M., Lesage, J.-J., & Litz, L. (2011). The concept of residuals for fault localization in discrete event systems. Control Engineering Practice, 19(9), 978-988. doi:10.1016/j.conengprac.2011.02.008Roth, M., Schneider, S., Lesage, J.-J., & Litz, L. (2012). Fault detection and isolation in manufacturing systems with an identified discrete event model. International Journal of Systems Science, 43(10), 1826-1841. doi:10.1080/00207721.2011.649369Chung-Hsien Kuo, & Han-Pang Huang. (2000). Failure modeling and process monitoring for flexible manufacturing systems using colored timed Petri nets. IEEE Transactions on Robotics and Automation, 16(3), 301-312. doi:10.1109/70.850648Ramirez-Trevino, A., Ruiz-Beltran, E., Aramburo-Lizarraga, J., & Lopez-Mellado, E. (2012). Structural Diagnosability of DES and Design of Reduced Petri Net Diagnosers. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 42(2), 416-429. doi:10.1109/tsmca.2011.2169950Cabasino, M. P., Giua, A., & Seatzu, C. (2014). Diagnosability of Discrete-Event Systems Using Labeled Petri Nets. IEEE Transactions on Automation Science and Engineering, 11(1), 144-153. doi:10.1109/tase.2013.2289360Yao, L., Feng, L., & Jiang, B. (2014). Fault Diagnosis and Fault Tolerant Control for Non-Gaussian Singular Time-Delayed Stochastic Distribution Systems. Mathematical Problems in Engineering, 2014, 1-9. doi:10.1155/2014/937583Murata, T. (1989). Petri nets: Properties, analysis and applications. Proceedings of the IEEE, 77(4), 541-580. doi:10.1109/5.24143Dotoli, M., Fanti, M. P., & Mangini, A. M. (2008). Real time identification of discrete event systems using Petri nets. Automatica, 44(5), 1209-1219. doi:10.1016/j.automatica.2007.10.014Muñoz, D. M., Correcher, A., García, E., & Morant, F. (2014). Identification of Stochastic Timed Discrete Event Systems with st-IPN. Mathematical Problems in Engineering, 2014, 1-21. doi:10.1155/2014/835312Latorre-Biel, J.-I., Jiménez-Macías, E., Pérez de la Parte, M., Blanco-Fernández, J., & Martínez-Cámara, E. (2014). Control of Discrete Event Systems by Means of Discrete Optimization and Disjunctive Colored PNs: Application to Manufacturing Facilities. Abstract and Applied Analysis, 2014, 1-16. doi:10.1155/2014/821707Cabasino, M. P., Giua, A., Lafortune, S., & Seatzu, C. (2012). A New Approach for Diagnosability Analysis of Petri Nets Using Verifier Nets. IEEE Transactions on Automatic Control, 57(12), 3104-3117. doi:10.1109/tac.2012.2200372Abdelwahed, S., Karsai, G., Mahadevan, N., & Ofsthun, S. C. (2009). Practical Implementation of Diagnosis Systems Using Timed Failure Propagation Graph Models. IEEE Transactions on Instrumentation and Measurement, 58(2), 240-247. doi:10.1109/tim.2008.200595

    Analysis of Petri Net Models through Stochastic Differential Equations

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    It is well known, mainly because of the work of Kurtz, that density dependent Markov chains can be approximated by sets of ordinary differential equations (ODEs) when their indexing parameter grows very large. This approximation cannot capture the stochastic nature of the process and, consequently, it can provide an erroneous view of the behavior of the Markov chain if the indexing parameter is not sufficiently high. Important phenomena that cannot be revealed include non-negligible variance and bi-modal population distributions. A less-known approximation proposed by Kurtz applies stochastic differential equations (SDEs) and provides information about the stochastic nature of the process. In this paper we apply and extend this diffusion approximation to study stochastic Petri nets. We identify a class of nets whose underlying stochastic process is a density dependent Markov chain whose indexing parameter is a multiplicative constant which identifies the population level expressed by the initial marking and we provide means to automatically construct the associated set of SDEs. Since the diffusion approximation of Kurtz considers the process only up to the time when it first exits an open interval, we extend the approximation by a machinery that mimics the behavior of the Markov chain at the boundary and allows thus to apply the approach to a wider set of problems. The resulting process is of the jump-diffusion type. We illustrate by examples that the jump-diffusion approximation which extends to bounded domains can be much more informative than that based on ODEs as it can provide accurate quantity distributions even when they are multi-modal and even for relatively small population levels. Moreover, we show that the method is faster than simulating the original Markov chain

    Petri nets for systems and synthetic biology

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    We give a description of a Petri net-based framework for modelling and analysing biochemical pathways, which uni¯es the qualita- tive, stochastic and continuous paradigms. Each perspective adds its con- tribution to the understanding of the system, thus the three approaches do not compete, but complement each other. We illustrate our approach by applying it to an extended model of the three stage cascade, which forms the core of the ERK signal transduction pathway. Consequently our focus is on transient behaviour analysis. We demonstrate how quali- tative descriptions are abstractions over stochastic or continuous descrip- tions, and show that the stochastic and continuous models approximate each other. Although our framework is based on Petri nets, it can be applied more widely to other formalisms which are used to model and analyse biochemical networks

    Integration of a failure monitoring within a hybrid dynamic simulation environment

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    The complexity and the size of the industrial chemical processes induce the monitoring of a growing number of process variables. Their knowledge is generally based on the measurements of system variables and on the physico-chemical models of the process. Nevertheless this information is imprecise because of process and measurement noise. So the research ways aim at developing new and more powerful techniques for the detection of process fault. In this work, we present a method for the fault detection based on the comparison between the real system and the reference model evolution generated by the extended Kalman filter. The reference model is simulated by the dynamic hybrid simulator, PrODHyS. It is a general object-oriented environment which provides common and reusable components designed for the development and the management of dynamic simulation of industrial systems. The use of this method is illustrated through a didactic example relating to the field of Chemical Process System Engineering

    Petri Nets at Modelling and Control of Discrete-Event Systems Containing Nondeterminism - Part 1

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    Discrete-Event Systems are discrete in nature, driven by discrete events. Petri Nets are one of the mostly used tools for their modelling and control synthesis. Place/Transitions Petri Nets, Timed Petri Nets, Controlled Petri Nets are suitable when a modelled object is deterministic. When the system model contains uncontrollable/unobservable transitions and unobservable/unmeasurable places or other failures, such kinds of Petri Nets are insufficient for the purpose. In such a case Labelled Petri Nets and/or Interpreted Petri Nets have to be used. Particularities and mutual differences of individual kinds of Petri Nets are pointed out and their applicability to modelling and control of Discrete-Event Systems are described and tested

    Identification of Stochastic Timed Discrete Event Systems with st-IPN

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    [EN] This paper presents amethod for the identification of stochastic timed discrete event systems, based on the analysis of the behavior of the input and output signals, arranged in a timeline. To achieve this goal stochastic timed interpreted Petri nets are defined.These nets link timed discrete event systems modelling with stochastic time modelling. The procedure starts with the observation of the input/output signals; these signals are converted into events, so that the sequence of events is the observed language. This language arrives to an identifier that builds a stochastic timed interpreted Petri net which generates the same language. The identified model is a deterministic generator of the observed language.The identification method also includes an algorithm that determines when the identification process is over.This work was supported by a Grant from the Universidad del Cauca, reference 2.3-31.2/05 2011.Muñoz-Añasco, DM.; Correcher Salvador, A.; García Moreno, E.; Morant Anglada, FJ. (2014). Identification of Stochastic Timed Discrete Event Systems with st-IPN. Mathematical Problems in Engineering. 2014:1-21. https://doi.org/10.1155/2014/835312S1212014Cassandras, C. G., & Lafortune, S. (Eds.). (2008). Introduction to Discrete Event Systems. doi:10.1007/978-0-387-68612-7Yingwei Zhang, Jiayu An, & Chi Ma. (2013). Fault Detection of Non-Gaussian Processes Based on Model Migration. IEEE Transactions on Control Systems Technology, 21(5), 1517-1526. doi:10.1109/tcst.2012.2217966Ichikawa, A., & Hiraishi, K. (s. f.). Analysis and control of discrete event systems represented by petri nets. Lecture Notes in Control and Information Sciences, 115-134. doi:10.1007/bfb0042308Fanti, M. P., Mangini, A. M., & Ukovich, W. (2013). Fault Detection by Labeled Petri Nets in Centralized and Distributed Approaches. IEEE Transactions on Automation Science and Engineering, 10(2), 392-404. doi:10.1109/tase.2012.2203596Cabasino, M. P., Giua, A., & Seatzu, C. (2010). Fault detection for discrete event systems using Petri nets with unobservable transitions. Automatica, 46(9), 1531-1539. doi:10.1016/j.automatica.2010.06.013Hu, H., Zhou, M., Li, Z., & Tang, Y. (2013). An Optimization Approach to Improved Petri Net Controller Design for Automated Manufacturing Systems. IEEE Transactions on Automation Science and Engineering, 10(3), 772-782. doi:10.1109/tase.2012.2201714Hu, H., Zhou, M., & Li, Z. (2011). Supervisor Optimization for Deadlock Resolution in Automated Manufacturing Systems With Petri Nets. IEEE Transactions on Automation Science and Engineering, 8(4), 794-804. doi:10.1109/tase.2011.2156783Hiraishi, K. (1992). Construction of a class of safe Petri nets by presenting firing sequences. Lecture Notes in Computer Science, 244-262. doi:10.1007/3-540-55676-1_14Estrada-Vargas, A. P., López-Mellado, E., & Lesage, J.-J. (2010). A Comparative Analysis of Recent Identification Approaches for Discrete-Event Systems. Mathematical Problems in Engineering, 2010, 1-21. doi:10.1155/2010/453254Shaolong Shu, & Feng Lin. (2013). I-Detectability of Discrete-Event Systems. IEEE Transactions on Automation Science and Engineering, 10(1), 187-196. doi:10.1109/tase.2012.2215959Li, L., & Hadjicostis, C. N. (2011). Least-Cost Transition Firing Sequence Estimation in Labeled Petri Nets With Unobservable Transitions. IEEE Transactions on Automation Science and Engineering, 8(2), 394-403. doi:10.1109/tase.2010.2070065Supavatanakul, P., Lunze, J., Puig, V., & Quevedo, J. (2006). Diagnosis of timed automata: Theory and application to the DAMADICS actuator benchmark problem. Control Engineering Practice, 14(6), 609-619. doi:10.1016/j.conengprac.2005.03.028Dotoli, M., Fanti, M. P., & Mangini, A. M. (2008). Real time identification of discrete event systems using Petri nets. Automatica, 44(5), 1209-1219. doi:10.1016/j.automatica.2007.10.014Chen, Y., Li, Z., Khalgui, M., & Mosbahi, O. (2011). Design of a Maximally Permissive Liveness- Enforcing Petri Net Supervisor for Flexible Manufacturing Systems. IEEE Transactions on Automation Science and Engineering, 8(2), 374-393. doi:10.1109/tase.2010.2060332Murata, T. (1989). Petri nets: Properties, analysis and applications. Proceedings of the IEEE, 77(4), 541-580. doi:10.1109/5.24143Ramirez-Trevino, A., Ruiz-Beltran, E., Aramburo-Lizarraga, J., & Lopez-Mellado, E. (2012). Structural Diagnosability of DES and Design of Reduced Petri Net Diagnosers. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 42(2), 416-429. doi:10.1109/tsmca.2011.2169950Ramirez-Trevino, A., Ruiz-Beltran, E., Rivera-Rangel, I., & Lopez-Mellado, E. (2007). Online Fault Diagnosis of Discrete Event Systems. A Petri Net-Based Approach. IEEE Transactions on Automation Science and Engineering, 4(1), 31-39. doi:10.1109/tase.2006.872120Toutenburg, H. (1974). Fleiss, J. L.: Statistical Methods for Rates and Proportions. John Wiley & Sons, New York-London-Sydney-Toronto 1973. XIII, 233 S. Biometrische Zeitschrift, 16(8), 539-539. doi:10.1002/bimj.19740160814Livingston, E. H., & Cassidy, L. (2005). Statistical Power and Estimation of the Number of Required Subjects for a Study Based on the t-Test: A Surgeon’s Primer. Journal of Surgical Research, 126(2), 149-159. doi:10.1016/j.jss.2004.12.013Ruppert, D. (2011). Statistics and Data Analysis for Financial Engineering. Springer Texts in Statistics. doi:10.1007/978-1-4419-7787-

    A proposal of an architecture for the coordination level of intelligent machines

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    The issue of obtaining a practical, structured, and detailed description of an architecture for the Coordination Level of Center for Intelligent Robotic Systems for Sapce Exploration (CIRSSE) Testbed Intelligent Controller is addressed. Previous theoretical and implementation works were the departure point for the discussion. The document is organized as follows: after this introductory section, section 2 summarizes the overall view of the Intelligent Machine (IM) as a control system, proposing a performance measure on which to base its design. Section 3 addresses with some detail implementation issues. An hierarchic petri-net with feedback-based learning capabilities is proposed. Finally, section 4 is an attempt to address the feedback problem. Feedback is used for two functions: error recovery and reinforcement learning of the correct translations for the petri-net transitions

    Dynamic production system identification for smart manufacturing systems

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    This paper presents a methodology, called production system identification, to produce a model of a manufacturing system from logs of the system's operation. The model produced is intended to aid in making production scheduling decisions. Production system identification is similar to machine-learning methods of process mining in that they both use logs of operations. However, process mining falls short of addressing important requirements; process mining does not (1) account for infrequent exceptional events that may provide insight into system capabilities and reliability, (2) offer means to validate the model relative to an understanding of causes, and (3) updated the model as the situation on the production floor changes. The paper describes a genetic programming (GP) methodology that uses Petri nets, probabilistic neural nets, and a causal model of production system dynamics to address these shortcomings. A coloured Petri net formalism appropriate to GP is developed and used to interpret the log. Interpreted logs provide a relation between Petri net states and exceptional system states that can be learned by means of novel formulation of probabilistic neural nets (PNNs). A generalized stochastic Petri net and the PNNs are used to validate the GP-generated solutions. The methodology is evaluated with an example based on an automotive assembly system

    An Executable System Architecture Approach to Discrete Events System Modeling Using SysML in Conjunction with Colored Petri Net

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    This paper proposes an executable system architecting paradigm for discrete event system modeling and analysis through integration of a set of architecting tools, executable modeling tools, analytical tools, and visualization tools. The essential step is translating SysML-based specifications into colored Petri nets (CPNs) which enables rigorous static and dynamic system analysis as well as formal verification of the behavior and functionality of the SysML-based design. A set of tools have been studied and integrated that enable a structured architecture design process. Some basic principles of executable system architecture for discrete event system modeling that guide the process of executable architecture specification and analysis are discussed. This paradigm is aimed at general system design. Its feasibility was demonstrated with a C4- type network centric system as an example. The simulation results was used to check the overall integrity and internal consistency of the architecture models, refine the architecture design, and, finally, verify the behavior and functionality of the system being modeled
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