Model based fault diagnosis for hybrid systems : application on chemical processes

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

Integration of a failure monitoring within a hybrid dynamic simulation environment

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

Dynamic state reconciliation and model-based fault detection for chemical processes

In this paper, we present a method for the fault detection based on the residual generation. The main idea is to reconstruct the outputs of the system from the measurements using the extended Kalman filter. The estimations are compared to the values of the reference model and so, deviations are interpreted as possible faults. The reference model is simulated by the dynamic hybrid simulator, PrODHyS. The use of this method is illustrated through an application in the field of chemical processe

Computer-aided HAZOP of batch processes

The modern batch chemical processing plants have a tendency of increasing technological complexity and flexibility which make it difficult to control the occurrence of accidents. Social and legal pressures have increased the demands for verifying the safety of chemical plants during their design and operation. Complete identification and accurate assessment of the hazard potential in the early design stages is therefore very important so that preventative or protective measures can be integrated into future design without adversely affecting processing and control complexity or capital and operational costs. Hazard and Operability Study (HAZOP) is a method of systematically identifying every conceivable process deviation, its abnormal causes and adverse hazardous consequences in the chemical plants. [Continues.

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-

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

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

Applications of Bayesian networks and Petri nets in safety, reliability, and risk assessments: A review

YesSystem safety, reliability and risk analysis are important tasks that are performed throughout the system lifecycle to ensure the dependability of safety-critical systems. Probabilistic risk assessment (PRA) approaches are comprehensive, structured and logical methods widely used for this purpose. PRA approaches include, but not limited to, Fault Tree Analysis (FTA), Failure Mode and Effects Analysis (FMEA), and Event Tree Analysis (ETA). Growing complexity of modern systems and their capability of behaving dynamically make it challenging for classical PRA techniques to analyse such systems accurately. For a comprehensive and accurate analysis of complex systems, different characteristics such as functional dependencies among components, temporal behaviour of systems, multiple failure modes/states for components/systems, and uncertainty in system behaviour and failure data are needed to be considered. Unfortunately, classical approaches are not capable of accounting for these aspects. Bayesian networks (BNs) have gained popularity in risk assessment applications due to their flexible structure and capability of incorporating most of the above mentioned aspects during analysis. Furthermore, BNs have the ability to perform diagnostic analysis. Petri Nets are another formal graphical and mathematical tool capable of modelling and analysing dynamic behaviour of systems. They are also increasingly used for system safety, reliability and risk evaluation. This paper presents a review of the applications of Bayesian networks and Petri nets in system safety, reliability and risk assessments. The review highlights the potential usefulness of the BN and PN based approaches over other classical approaches, and relative strengths and weaknesses in different practical application scenarios.This work was funded by the DEIS H2020 project (Grant Agreement 732242)