Discrete Event Model-Based Approach for Fault Detection and Isolation of Manufacturing Systems

International audienceThis paper presents a discrete event model-based approach for Fault Detection and Isolation of manufacturing systems. This approach considers a system as a set of independent plant elements. Each plant element is composed of a set of interrelated Parts of Plant (PoPs) modeled by a Moore automaton. Each PoP model is only aware of its local behavior. The degraded and faulty behaviors are added to each PoP model in order to obtain extended PoP ones. An extrapolation of Gaussian learning is realized to obtain acceptable temporal intervals between the time occurrences of correlated events. Finally based on the PoP extended models and the links between them, a fault candidates' tree is established for each plant element. This candidates' tree corresponds to a local on-line fault event occurrence observer, called diagnoser. Thus, the diagnosis decision is distributed on each plant element. An application example is used to illustrate the approach

Unconditional decentralized structure for the fault diagnosis of discrete event systems

International audienceThis paper proposes an unconditional decentralized structure to realize the fault diagnosis of Discrete Event Systems (DES), specially manufacturing systems with discrete sensors and actuators. This structure is composed on the use of a set of local diagnosers, each one of them is responsible of a specific part of the plant. These local diagnosers are based on a modular modelling of the plant in order to reduce the state explosion. Each local diagnoser uses event-based, state based and timed models to take a decision about fault's occurrences. These models are obtained using the information provided by the plant, the controller and the actuators reactivity. All local diagnosis decisions are then merged by a Boolean operator in order to obtain one global diagnosis decision. Finally, the diagnosers are polynomial-time in the cardinality of the state space of the system. This approach is illustrated using an example of manufacturing system

A new approach for diagnosability analysis of Petri nets using Verifier Nets

In this paper, we analyze the diagnosability properties of labeled Petri nets. We consider the standard notion of diagnosability of languages, requiring that every occurrence of an unobservable fault event be eventually detected, as well as the stronger notion of diagnosability in K steps, where the detection must occur within a fixed bound of K event occurrences after the fault. We give necessary and sufficient conditions for these two notions of diagnosability for both bounded and unbounded Petri nets and then present an algorithmic technique for testing the conditions based on linear programming. Our approach is novel and based on the analysis of the reachability/coverability graph of a special Petri net, called Verifier Net, that is built from the Petri net model of the given system. In the case of systems that are diagnosable in K steps, we give a procedure to compute the bound K. To the best of our knowledge, this is the first time that necessary and sufficient conditions for diagnosability and diagnosability in K steps of labeled unbounded Petri nets are presented

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

On the cost of diagnosis with disambiguation

International audienceDiagnosis consists in deciding from a partial observation of a system whether a fault has occurred. A system is diagnosable if there exists a mechanism (a diagnoser) that accurately detects faults a finite number of steps after their occurrence. In a regular setting, a diagnoser builds an estimation of possible states of the system after an observation to decide if a fault has occurred. This paper addresses diagnosability (deciding whether a system is diagnosable) and its cost for safe Petri nets. We define an energy-like cost model for Petri nets: transitions can consume or restore energy of the system. We then give a partial order representation for state estimation, and extend the cost model and the capacities of diagnosers. Diagnosers are allowed to use additional energy to refine their estimations. Diagnosability is then seen as an energy game: checking whether disambiguation mechanisms are sufficient to allow diagnosability is in 2-EXPTIME, and one can also decide whether diagnosability under budget constraint holds in 2-EXPTIME

Distributed mode estimation through constraint decomposition

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 131-134).Large-scale autonomous systems such as modern ships or spacecrafts require reliable monitoring capabilities. One of the main challenges in large-scale system monitoring is the difficulty of reliably and efficiently troubleshooting component failure and deviant behavior. Diagnosing large-scale systems is difficult because of the fast increase in combinatorial complexity. Hence, efficient problem encoding and knowledge propagation between time steps is crucial. Moreover, concentrating all the diagnosis processing power in one machine is risky, as it creates a potential critical failure point. Therefore, we want to distribute the online estimation procedure. We introduce here a model-based method that performs robust, online mode estimation of complex, hardware or software systems in a distributed manner. Prior work introduced the concept of probabilistic hierarchical constraint automata (PHCA) to compactly model both complex software and hardware behavior. Our method, inspired by this previous work, translates the PHCA model to a constraint representation. This approach handles a more precise initial state description, scales to larger systems, and to allow online belief state updates. Additionally, a tree-clustering of the dual constraint graph associated with the multi-step trellis diagram representation of the system makes the search distributable. Our search algorithm enumerates the optimal solutions of a hard-constraint satisfaction problem in a best-first order by passing local constraints and conflicts between neighbor sub-problems of the decomposed global problem. The solutions computed online determine the most likely trajectories in the state space of a system. Unlike prior work on distributed constraint solving, we use optimal hard constraint satisfaction problems to increase encoding compactness. We present and demonstrate this approach on a simple example and an electric power-distribution plant model taken from a naval research project involving a large number of modules. We measure the overhead caused by distributing mode estimation and analyze the practicality of our approach.by Henri Badaro.S.M

Advances in Robotics, Automation and Control

The book presents an excellent overview of the recent developments in the different areas of Robotics, Automation and Control. Through its 24 chapters, this book presents topics related to control and robot design; it also introduces new mathematical tools and techniques devoted to improve the system modeling and control. An important point is the use of rational agents and heuristic techniques to cope with the computational complexity required for controlling complex systems. Through this book, we also find navigation and vision algorithms, automatic handwritten comprehension and speech recognition systems that will be included in the next generation of productive systems developed by man