Automated modelling of reactive discrete event systems from external behavioural data

International audienceThis paper deals with automated modelling of reactive discrete event systems (DES). A software tool for building automatically interpreted Petri net models from an observed system's input/output sequence is presented. The tool is based on a black-box identification method that processes the input/output sequence, and synthesises and draws the model corresponding to such a sequence. First, the identification method is outlined; then the developed software is described and applied to an illustrative example from the manufacturing area

Identification of Stochastic Timed Discrete Event Systems with st-IPN

[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 Comparative Analysis of Recent Identification Approaches for Discrete-Event Systems

Analogous to the identification of continuous dynamical systems, identification of discrete-event systems (DESs) consists of determining the mathematical model that describes the behaviour of a given ill-known or eventually unknown system from the observation of the evolution of its inputs and outputs. First, the paper overviews identification approaches of DES found in the literature, and then it provides a comparative analysis of three recent and innovative contributions

Stochastic DES Fault Diagnosis with Coloured Interpreted Petri Nets

[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

Verification and Anomaly Detection for Event-Based Control of Manufacturing Systems.

Many important systems can be described as discrete event systems, including a manufacturing cell and patient flow in a clinic. Faults often occur in these systems and addressing these faults is important to ensure proper functioning. There are two main ways to address faults. Faults can be prevented from ever occurring, or they can be detected at the time at which they occur. This work develops methods to address faults in event-based systems for which there is no formal, pre-existing model. A primary application is manufacturing systems, where reducing downtime is especially important and pre-existing formal models are not commonly available. There are three main contributions. The first contribution is formalizing input order robustness - inputs occurring in different orders and yielding the same final state and set of outputs - and creating a method for its verification for logic controllers and networks of controllers. Theory is developed for a class of networks of controllers to be verified modularly, reducing the computational complexity. Input order robustness guarantees determinism of the closed-loop system. The second contribution is an anomaly detection solution for event-based systems without a pre-existing formal model. This solution involves model generation, performance assessment, and anomaly detection itself. A new variation of Petri nets was created to model the systems in this solution that incorporates resources in a less restrictive way. The solution detects anomalies and provides information about when the anomaly was first observed to help with debugging. The third contribution is the identification and resolution of five inconsistencies found between typical academic assumptions and industry practice when applying the anomaly detection solution to an industrial system. Resolutions to the inconsistencies included working with industry collaborators to change logic, and developing new algorithms to incorporate into the anomaly detection solution. Through these resolutions, the anomaly detection solution was improved to make it easier to apply to industrial systems. These three contributions for handling faults will help reduce down-time in manufacturing systems, and hence increase productivity and decrease costs.Ph.D.Electrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/78897/1/lzallen_1.pd

Deterministic Generation of Regular Languages in Discrete Event Systems

[ES] En este artículo se propone una red de Petri, interpretada, estocástica, (st-IPN), como modelo para representar el lenguaje regular obtenido a partir de la combinación de señales de entrada - salida, en un sistema de eventos discretos (SED) en lazo cerrado. Las señales de entrada, son las señales externas que afecten al sistema y las órdenes de control emitidas por el controlador a la planta y las señales de salida son las respuestas de los sensores a las órdenes de control. La st-IPN propuesta, es un generador determinista del lenguaje legal de sistema, capaz de representar secuencias de eventos temporizados de naturaleza estocástica. El modelo propuesto puede ser aplicado a sistemas de gran escala, a partir de la división del sistema en subsistemas, ya que el modelo global puede ser encontrado con base en la composición de los modelos de los subsistemas.[EN] In this paper is proposed a stochastic interpreted Petri net, (st-IPN) as a model to represent the regular language derived from the combination of input signals in a Discrete Event System (DES) in closed loop. The input signals are external signals affecting the system and the control commands issued by the controller to the plant and the output signals are the responses of the sensors to the control commands. The st-IPN proposed is a deterministic generator of the system legal language able to represent sequences of stochastic timed events. The proposed model can be applied to large-scale systems, from the division of the system into subsystems, since the global model can be a composition of the subsystems models.Este trabajo ha sido realizado parcialmente gracias a la comisión académica financiada por la Universidad del Cauca, referencia 2.3-31.2/05 2011.Muñoz, DM.; Correcher Salvador, A.; García Moreno, E.; Morant Anglada, FJ. (2016). Generación Determinística de Lenguajes Legales para Sistemas de Eventos Discretos. Revista Iberoamericana de Automática e Informática industrial. 13(2):207-219. https://doi.org/10.1016/j.riai.2016.01.002OJS207219132Ashley, J., & Holloway, L. E. (2004). Qualitative Diagnosis of Condition Systems. Discrete Event Dynamic Systems, 14(4), 395-412. doi:10.1023/b:disc.0000039787.51382.afBasile, F., Chiacchio, P., Coppola, J., De Tommasi, G., june 2011. Identification of petri nets using timing information. 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