An Intelligent Technique Based on Petri Nets for Diagnosability Enhancement of Discrete Event Systems

  • YuanLin Wen
  • MuDer Jeng
  • LiDer Jeng
  • Fan Pei-Shu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4252)


This paper presents an intelligent systematic methodology for enhancing diagnosability of discrete event systems by adding sensors. The methodology consists of the following iteractive steps. First, Petri nets are used to model the target system. Then, an algorithm of polynomial complexity is adopted to analyze a sufficient condition of diagnosability of the modeled system. Here, diagnosability is defined in the context of the discrete event systems theory, which was first introduced by Sampath [3]. If the system is found to be possibly non-diagnosable, T-components of the Petri net model are computed to find a location in the system for adding a sensor. The objective is to distinguish multiple T-components with the same observable event sequences. The diagnosability-checking algorithm is used again to see if the system with the newly added sensor is diagnosable. The process is repeated until either the system is diagnosable or diagnosability of the system cannot be enhanced.


Failure Event Intelligent Technique Polynomial Complexity Discrete Event System Fault Event 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Jiang, S., Kumar, R., Garcia, H.E.: Optimal Sensor Selection for discrete-Event Systems with Partial Observation. IEEE Trans. on Automatic Control 48(3), 369–381Google Scholar
  2. 2.
    Jiang, S., Huang, Z., Chandra, V., Kumar, R.: A Polynomial Algorithm for Testing Diagnosability of Discrete-Event Systems. IEEE Trans. on Automatic Control 46(8), 1318–1321Google Scholar
  3. 3.
    Sampath, M., Lafortune, S., Sinnamohideen, K., Teneketzis, D.: Diagnosability of Discrete-Event Systems. IEEE Trans. on Automatic Control 40(9), 1555–1557Google Scholar
  4. 4.
    Wen, Y.L., Jeng, M.D., Huang, Y.S.: Diagnosability of Semiconductor Manufacturing Equipment. Material Science Forum 505, 1135–1140Google Scholar
  5. 5.
    Peterson, J.L.: Petri Net Theory and the Modeling of Systems. Prentice-Hall, Englewood Cliffs (1981)Google Scholar
  6. 6.
    Hall, D.L., Llinas, J.: An introduction to multisensor data fusion. Proceedings of the IEEE 85(1), 6–23Google Scholar
  7. 7.
    Desel, J., Esparza, J.: Free Choice Petri Nets. Cambridge University Press, Cambridge (1995)MATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • YuanLin Wen
    • 1
  • MuDer Jeng
    • 1
  • LiDer Jeng
    • 2
  • Fan Pei-Shu
    • 3
  1. 1.Department of Electrical EngineeringNational Taiwan Ocean UniversityKeelungTaiwan
  2. 2.Department of Electrical EngineeringChung-Yuan Christian UniversityChung-LiTaiwan
  3. 3.College of Mechanica and Electrical EngineeringNational Taipei University of TechnologyTaipeiTaiwan

Personalised recommendations