Analysis of Autonomic Service Oriented Architecture

— Service-Oriented Architecture (SOA) enables composition of large and complex computational units out of the available atomic services. However, implementation of SOA, for its dynamic nature, could bring about challenges in terms of service discovery, service interaction, and service composition. SOA may often need to dynamically re-configure and re-organize its topologies of interactions between the web services because of some unpredictable events, such as crashes or network problems, which will cause service unavailability. Complexity and dynamism of the current and future global network systems require service architecture that is capable of autonomously changing its structure and functionality to meet dynamic changes in the requirements and environment with little human intervention. In this paper, formal models of a proposed autonomic SOA framework are developed and analyzed using Petri Net. The results showed that SOA can be improved to cope with dynamic environment and services unavailability by incorporating case-based reasoning and autonomic computing paradigm to monitor and analyze events and service requests, then to plan and execute the appropriate actions using the knowledge stored in knowledge database. Keywords— Service Oriented Architecture, autonomic computing, case-based reasoning, formal model, Petri Ne

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

Industrial Applications: New Solutions for the New Era

This book reprints articles from the Special Issue "Industrial Applications: New Solutions for the New Age" published online in the open-access journal Machines (ISSN 2075-1702). This book consists of twelve published articles. This special edition belongs to the "Mechatronic and Intelligent Machines" section

Survivability modeling for cyber-physical systems subject to data corruption

Cyber-physical critical infrastructures are created when traditional physical infrastructure is supplemented with advanced monitoring, control, computing, and communication capability. More intelligent decision support and improved efficacy, dependability, and security are expected. Quantitative models and evaluation methods are required for determining the extent to which a cyber-physical infrastructure improves on its physical predecessors. It is essential that these models reflect both cyber and physical aspects of operation and failure. In this dissertation, we propose quantitative models for dependability attributes, in particular, survivability, of cyber-physical systems. Any malfunction or security breach, whether cyber or physical, that causes the system operation to depart from specifications will affect these dependability attributes. Our focus is on data corruption, which compromises decision support -- the fundamental role played by cyber infrastructure. The first research contribution of this work is a Petri net model for information exchange in cyber-physical systems, which facilitates i) evaluation of the extent of data corruption at a given time, and ii) illuminates the service degradation caused by propagation of corrupt data through the cyber infrastructure. In the second research contribution, we propose metrics and an evaluation method for survivability, which captures the extent of functionality retained by a system after a disruptive event. We illustrate the application of our methods through case studies on smart grids, intelligent water distribution networks, and intelligent transportation systems. Data, cyber infrastructure, and intelligent control are part and parcel of nearly every critical infrastructure that underpins daily life in developed countries. Our work provides means for quantifying and predicting the service degradation caused when cyber infrastructure fails to serve its intended purpose. It can also serve as the foundation for efforts to fortify critical systems and mitigate inevitable failures --Abstract, page iii