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A realtime fuzzy Petri net diagnoser for detecting progressive faults in PLC based discrete manufacturing system

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Abstract

In this paper, we explored a realtime fuzzy Petri net approach to diagnose progressive faults in discrete manufacturing systems. Progressive faults are usually caused by deterioration or aging and show stochastic properties. Some researchers have reported how to detect abrupt faults in discrete manufacturing systems using Petri net. However, little research has been conducted on Petri net diagnoser to progressive faults in discrete manufacturing event systems. To tackle this problem, we explored an approach including a realtime Petri net model and a fuzzy Petri net diagnoser to replicate the plant and detect faults in discrete manufacturing systems. The realtime Petri net model monitors events generated from the discrete manufacturing system, also compares the outputs and pre-settings. Once a difference is detected, it will start the fuzzy Petri net diagnoser to locate faults. For the purpose of validation, this approach was implemented with Visual Basic for diagnosing a dual robot arm. Evaluation experiments validated the diagnoser's performance on accuracy and diagnosability. It illustrated that the proposed approach can have a high accuracy rate of 93% and maximum diagnosis delay of eight steps; it proves that the approach has the capability of integrating knowledge and handling uncertainties. It also remedies the nonsynchronization between the diagnoser and the plant. The approach to construct the model and diagnoser is systematic; it has an excellent projection on intermittent fault diagnosis and hybrid systems.

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References

  1. Inagaki S, Suzuki T, Saito M, Aoki T (2007) Local/global fault diagnosis of event-driven systems based on Bayesian Network and Timed Markov Mode. SICE Annual Conference 2007:540–545. doi:10.1109/SICE.2007.4421042

    Article  Google Scholar 

  2. Wang X, Chattopadhyay I, Ray A (2004) Probabilistic fault diagnosis in discrete event systems. 43rd IEEE Conference on Decision and Control 5:4794–4799

    Google Scholar 

  3. Ruiz-Beltran E, Jimenez-Ochoa I, Ramirez-Trevino A, Lopez-Mellado E, Meda-Campana M (2005) Fault detection and location in DES using Petri nets. IEEE International Conference on Systems, Man and Cybernetics 2:1645–1650

    Article  Google Scholar 

  4. Looney CG (1988) Fuzzy Petri nets for rule-based decision making. IEEE Transactions on Systems Man and Cybernetics 18(1):178–183

    Article  Google Scholar 

  5. Zhou MC, Venkatesh K (1998) Modeling, simulation and control of flexible manufacturing systems: a Petri net approach. World Scientific, Singapore

    Google Scholar 

  6. Qunming Li, Ling Zhu, Zhen Xu (2007) Fuzzy Petri-nets based fault diagnosis for mechanical-electric equipment. 2007 IEEE International Conference on Control and Automation, Guangzhou, China, May 30 to June 1, 2007, P2539–2543

  7. Sun J, Qin S-Y, Song Y-H (2004) Fault diagnosis of electric power systems based on fuzzy Petri nets. IEEE Transactions on Power Systems 19(4):2053–2059

    Article  Google Scholar 

  8. Ushio T, Onishi I, Okuda K (1998) Fault detection based on Petri net models with faulty behaviors. IEEE International Conference on Systems, Man, and Cybernetics 1:113–118

    Google Scholar 

  9. Chung S-L, Chien-Chung Wu, Jeng MuDer (2003) Failure diagnosis: a case study on modeling and analysis by Petri nets. IEEE International Conference on Systems, Man, and Cybernetics 3:2727–2732

    Google Scholar 

  10. Chung SL (2005) Diagnosing PN-based models with partial observable transitions. Int J Comput Integr Manuf 18:158–169

    Article  Google Scholar 

  11. Lefebvre D, Delherm C (2007) Diagnosis of DES with Petri net models. IEEE Transactions on Automation Science and Engineering 4(1):114–118

    Article  Google Scholar 

  12. Yen J, Langari R (1999) Fuzzy logic: intelligence, information, and control. Prentice-Hall, Englewood Cliffs, NJ

    Google Scholar 

  13. Yu Ru, Hadjicostis CN (2009) Fault diagnosis in discrete event systems modeled by partially observed Petri nets. Discrete Event Dyn Syst 19:551–575

    Article  MathSciNet  MATH  Google Scholar 

  14. Chung-Hsien Kuo and 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

    Article  Google Scholar 

  15. Angel M, Martínez T, Garcia Moreno E (2008) Fault diagnosis and modeling of the liquids packaging process. A research based on Petri nets. 10th International Conference on Control Automation, Robotics and Vision 2008:1620–1624

    Google Scholar 

  16. Wen YuanLin, Chung ShengLuen, Jeng LiDer, Jeng MuDer (2007) Intelligent design of diagnosable systems: a case study of semiconductor manufacturing machines. Knowledge-Based Intelligent Information and Engineering Systems, Part II LNAI 4693:877–884

    Article  Google Scholar 

  17. Prock J (1991) A new technique for fault detection using Petri nets. Automatica 27(2):239–245

    Article  MathSciNet  Google Scholar 

  18. Yao Albert Wen-Long, Liao Hsin-Te, Chi S-C, Peng S-S (2005) A Petri net based offline simulation and online diagnostic platform for manufacturing system. Journal of the Chinese Institute of Industrial Engineers 22(1):64–75

    Article  Google Scholar 

  19. Yao AWL (2005) Design and implementation of Web-based diagnosis and management system for an FMS. Int J Adv Manuf Technol 26:1379–1387

    Article  Google Scholar 

  20. Hongge H, Dagui H, Hong H, Guangying S (2004) Combining modeling and fault detection in automated manufacturing systems based on hybrid Petri net. Proceedings of the 2004 International Conference on Intelligent Mechatronics and Automation 2004:728–732

    Google Scholar 

  21. S Genc, S Lafortune (2006) Distributed diagnosis of places-boundered Petri nets. Department of Electrical Engineering and Computer Science, University of Michigan, USA

  22. Zhenhua Wu, Sheng-jen Hsieh (2010) Application of hidden Markov model and sequential function chart to detect fault in discrete manufacturing system. 2010 International Symposium on Flexible Automation, July 12–14, 2010 Tokyo, Japan

  23. Cassandras CG, Lafortune S (2008) Introduction to discrete event systems, 2nd edn. Springer, Heidelberg

    Book  MATH  Google Scholar 

  24. Sampath M, Sengupta R, Lafortune S, Sinnamohideen K, Teneketzis D (1995) Diagnosability of discrete event systems. IEEE Transaction on Automatic Control 40(9):1555–1575

    Article  MathSciNet  MATH  Google Scholar 

  25. Sayed-Mouchaweh M, Philippot A, Carre-Menetrier V (2008) Decentralized diagnosis based on Boolean discrete discrete event models: application on manufacturing systems. International Journal of Production Research 46(19):5469–5490

    Article  MATH  Google Scholar 

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Authors and Affiliations

  1. Mechanical Engineering Department, Texas A&M University, College Station, TX, 77841, USA

    Zhenhua Wu & Sheng-Jen Hsieh

  2. Department of Engineering Technology and Industrial Distribution, Texas A&M University, College Station, TX, 77841, USA

    Sheng-Jen Hsieh

Authors
  1. Zhenhua Wu
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  2. Sheng-Jen Hsieh
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Correspondence to Zhenhua Wu.

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Wu, Z., Hsieh, SJ. A realtime fuzzy Petri net diagnoser for detecting progressive faults in PLC based discrete manufacturing system. Int J Adv Manuf Technol 61, 405–421 (2012). https://doi.org/10.1007/s00170-011-3689-4

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  • DOI: https://doi.org/10.1007/s00170-011-3689-4

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