Journal of Intelligent Manufacturing

, Volume 29, Issue 8, pp 1891–1904 | Cite as

On-line fault diagnosis of FMS based on flows analysis

  • Olfa FakhfakhEmail author
  • Armand Toguyeni
  • Ouajdi Korbaa


Any flexible manufacturing system (FMS) may face fault which may disrupt the production and cause delays. Thus, the identification of the source of failure is very important to intervene rapidly. This paper aims to develop an indirect and incremental diagnostic approach to identify the root cause of the observed delay in the context of a single fault occurrence. In this study the observation is done only at the output of the system to measure the output dates of each part and to detect the eventual delay. For this purpose, a mathematical model is developed to model the proposed diagnostic approach of FMS under cyclic scheduling. This cyclic approach provides intermediary reference points to detect any discrepancy with regard the predictive scheduling. These intermediary reference points correspond to the end of each cycle defined by the scheduling. To solve this problem, the constraint programming technique is used. Finally, the performance of the proposed approach is evaluated with respect to the literature. The major merit of this study is to prove the capacity to diagnose efficiently the progressive faults of a plant without the necessity to add sensors dedicated to its monitoring.


On-line fault diagnosis Indirect diagnosis Flexible manufacturing system Cyclic scheduling Constraint programming 


  1. Basile, F., Chiacchio, P., & Tommasi, G. D. (2012). On k diagnosability of petri nets via integer linear programming. Automatica, 48(9), 2047–2058.CrossRefGoogle Scholar
  2. Belkahla, O., Yim, P., Korbaa, O., & Ghedira, K. (2007). A distributed transient interproduction scheduling for flexible manufacturing systems. Journal Européen des Systèmes Automatisés, 41(1), 101–123.CrossRefGoogle Scholar
  3. Benamar, A., Camus, H., & Korbaa, O. (2011). Mathematical model for cyclic scheduling with work-in-process minimization. Journal of Flexible Services and Manufacturing, 23(2), 111–136.CrossRefGoogle Scholar
  4. Bohm, S., Haar, S., Haddad, S., Hofman, P., & Schwoon, S. (2015). Active diagnosis with observable quiescence. In 54th IEEE Conference on Decision and Control (CDC’15).Google Scholar
  5. Cassandras, C., & Lafortune, S. (2008). Introduction to discrete event systems (2nd ed.). Berlin: Springer.CrossRefGoogle Scholar
  6. Chiacchio, P., & Tommasi, G. (2009). An efficient approach for online diagnosis of discrete event systems. IEEE Transactions On Automatic Control, 54(4), 748–759.CrossRefGoogle Scholar
  7. Ding, S. (2008). Model-based fault diagnosis techniques: Design schemes, algorithms, and tools. Berlin: Springer.Google Scholar
  8. Fakhfakh, O., Korbaa, O., & Toguyeni, A. (2012). Double chaining approach for indirect monitoring of fms under cyclic scheduling. Information Control Problems in Manufacturing, 14(1), 151–157.Google Scholar
  9. Giua, A. (2015). Diagnosis and diagnosability of discrete event systems using petri nets. 9th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes SAFEPROCESS 2015 - Paris 48 (21), 179.CrossRefGoogle Scholar
  10. Grastien, A., & Anbulagan, A. (2010). Diagnostic de systèmes à evénéments discrets à base de cohérence par sat. Revue d’Intelligence Artificielle, 24(6), 757–786.CrossRefGoogle Scholar
  11. He, S., He, Z., & Wang, G. A. (2013). Online monitoring and fault identification of mean shifts in bivariate processes using decision tree learning techniques. Journal of Intelligent Manufacturing, 24(1), 25–34.CrossRefGoogle Scholar
  12. Herroelen, W., & Leus, R. (2004). Robust and reactive project scheduling: A review and classification of procedures. International Journal of Production Research, 42(8), 1599–1620.CrossRefGoogle Scholar
  13. Hsu, T., Korbaa, O., Dupas, R., & Goncalves, G. (2008). Cyclic scheduling for fms: Modelling and evolutionary solving approach. European Journal of Operational Research, 191(2), 464–484.CrossRefGoogle Scholar
  14. Isermann, R. (2011). Fault-diagnosis applications. Model-based condition monitoring: Actuators, drives, machinery, plants, sensors, and fault-tolerant systems (1st ed.). Berlin: Springer.CrossRefGoogle Scholar
  15. Korbaa, O., Camus, O., & Gentina, J. C. (2002). A new cyclic scheduling algorithm for flexible manufacturing systems. International Journal of Flexible Manufacturing Systems, 14(2), 173–187.CrossRefGoogle Scholar
  16. Ladiges, J., Haubeck, C., Fay, A., & Lamersdorf, W. (2015). Learning behaviour models of discrete event production systems from observing input/output signals. 15th IFAC Symposium on Information Control Problems in Manufacturing 48 (3), 1565–1572.CrossRefGoogle Scholar
  17. Lei, W., & Yuen, C. (2012). Formulation of a novel production line monitoring technique. International Journal of Production Research, 50(22), 6612–6623.CrossRefGoogle Scholar
  18. Ly, F., Toguyeni, A., & Craye, E. (2000). Indirect predictive monitoring in fms. Robotics and computer integrated manufacturing, 16(5), 321–338.CrossRefGoogle Scholar
  19. Mortada, M., Yacout, S., & Lakis, A. (2014). Fault diagnosis in power transformers using multi-class logical analysis of data. Journal of Intelligent Manufacturing, 25(6), 1429–1439.CrossRefGoogle Scholar
  20. Nabli, L. (2000). Surveillance prédictive conditionnelle prévisionnelle indirecte d’une unité de filature textile : Approche par la qualité. Ph.D. thesis, Ecole Centrale de Lille.Google Scholar
  21. Pratap, S., Daultani, Y., Tiwari, M. K., & Mahanty, B. (2015). Rule based optimization for a bulk handling port operations.Journal of Intelligent Manufacturing. doi: 10.1007/s10845-015-1108-7.CrossRefGoogle Scholar
  22. Roth, M., Schneider, S., Lesage, 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.CrossRefGoogle Scholar
  23. Ru, Y., & Hadjicostis, C. (2009). Fault diagnosis in discrete event systems modeled by partially observed petri nets. Discrete Event Dynamic Systems, 19(4), 551–575.CrossRefGoogle Scholar
  24. Sampath, M., Sengupta, R., Lafortune, S., & Sinnamoh, K. (1995). Diagnosability of discrete-event systems. IEEE Transactions on Automatic Control, 40(9), 1555–1575.CrossRefGoogle Scholar
  25. Sampath, M., Sengupta, R., Sinnamohideen, K., Lafortune, S., & Teneketzis, D. (1996). Failure diagnosis using discrete event systems. IEEE Transaction on Control System Technology, 4(2), 105–124.CrossRefGoogle Scholar
  26. Sayed-Mouchaweh, M., Philippot, A., & Carre-Menetrier, V. (2008). Decentralized diagnosis based on boolean discrete event models: Application on manufacturing systems. International Journal of Production Research, 46(19), 5469–5490.CrossRefGoogle Scholar
  27. Son, H., & Lee, S. (2007). Failure diagnosis and recovery based on des framework. Journal of Intelligent Manufacturing, 18(2), 249–260.CrossRefGoogle Scholar
  28. Staroswiecki, M., & Comtet-Varga, G. (2001). Analytic redundancy relations for fault detection and isolation in algebraic dynamic systems. Automatica, 37(5), 687–699.CrossRefGoogle Scholar
  29. Toguyeni, A., Craye, E., & Gentina, J. (1997). Time and reasoning for on-line diagnosis of failures in flexible manufacturing systems. In Proceedings of the 15th IMACS world congress on scientific computation, modeling, and applied mathematics 6, 709–714.Google Scholar
  30. Toguyeni, A., & Korbaa, O. (2005). Indirect monitoring of the failures of a Flexible Manufacturing Systems under cyclic scheduling. Robotics and Computer-Integrated Manufacturing, 21(1), 1–10.CrossRefGoogle Scholar
  31. User’s Manual (2010). IBM ILOG Solver V6.8.Google Scholar
  32. Valentin, C. (1994). Modeling and analysis methods for a class of hybrid dynamic systems. Symposium Automatisation des Processus Mixtes: Les Systèmes Dynamiques Hybrides pp. 221–226.Google Scholar
  33. Verdiere, N., Jauberthie, C., & Trave-Massuyes, L. (2015). Functional diagnosability and detectability of nonlinear models based on analytical redundancy relations. Journal of Process Control, 35, 1–10.CrossRefGoogle Scholar
  34. Zaytoon, J., & Lafortune, S. (2013). Overview of fault diagnosis methods for discrete event systems. Annual Reviews in Control, 37(2), 308–320.CrossRefGoogle Scholar
  35. Zhang, K., Yuan, H., & Nie, P. (2015). A method for tool condition monitoring based on sensor fusion. Journal of Intelligent Manufacturing, 26(5), 1011–1026.CrossRefGoogle Scholar
  36. Zwingelstein, G. (1995). Diagnostic des défaillances - théorie et pratique pour les systèmes industriels. Traité des Nouvelles Technologies, série Diagnostic et Maintenance. Hermès Science, France.Google Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Ecole Nationale des Sciences de l’InformatiqueMARS Research-Unit, Campus Universitaire de la ManoubaManoubaTunisia
  2. 2.Ecole Centrale de LilleCRIStALVilleneuve d’AscqFrance
  3. 3.ISITCom Hammam-SousseMARS Research-UnitSousseTunisia

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