Advertisement

Real-time fault diagnosis — Using occupancy grids and neural network techniques

  • Amit Kumar Ray
  • R. B. Misra
Fault Diagnosis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 604)

Abstract

This paper presents a methodology for real-time fault diagnosis of manufacturing systems using occupancy grids and neural network techniques. Themain advantages ofthe system over other existing methods are its ability to capture imprecise and time dependent information, ability to accommodate nonlinear relationships, ability to learn and acquire knowledge automatically. A case study related to real-time milling machine fault diagnosis is discussed. The paper also discusses the problems with the proposed method and the future research directions.

Keywords

Neural networks Occupancy grids Information measure Back-propagation algorithm 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    Monostori, L., 1988, “Signal processing and Decision Making in Machine tool monitoring systems”, Proc. of Manufacturing International, 1988, Vol. 1, pp. 277–284.Google Scholar
  2. [2]
    Chen, Y.B., Sha,J.L. and Wu,S.M., 1990,”Diagnosis of the Tapping Process by Information Measure and Probability Voting Approach”, ASME journal of En gineering for Industry, Vol. 112, Nov., pp. 319–325.Google Scholar
  3. [3]
    Devijver, P.A. and Kittler,J., Patteren Recognition: A statistical Approach, Printice Hall, 1982.Google Scholar
  4. [4]
    Hoskins, J.C. and Himmelblau, D.M, 1990, “Fault detection and diagnosis using Artificial neural networks”, in the Mavrovouniotis, M.L. (Eds.), “Artificial Intelligence in Process Engineering”, Academic Press Inc., pp. 123–160.Google Scholar
  5. [5]
    Peng,Y., and Reggia,J.A.,1989 “A Connectionist model fordiagnostic problem solving”, IEEE Trans. on Systems man and cybernetics, vol. 19,no.2, March/April, pp.285–298.CrossRefGoogle Scholar
  6. [6]
    Ray, A.K., 1991, “Equipment Fault Diagnosis — A Neural Network Approach”, Computers in Industray an International Journal, Vol. 16, No. 2,pp. 169–177 June 1991.Google Scholar
  7. [7]
    Rumelhart,D.E., Hinton,G.E., and Williams,R.J., 1986 “Learning internal rep resentation by error propagation” in Rumelhart, D.E., and McClell and, J.L.,(Eds.),”Tarallel Distribution Processing”, Cambridge, MA: MIT press, chap.8,pp. 318–362.Google Scholar
  8. [8]
    Bebis, G.N. and Papadourakis,G.M., “Object recognition using invariant object boundary representations and neural network models”,vol. 25, no. 1, pp. 25–44, 1992.Google Scholar
  9. [9]
    Elfes, A., 1989, “Using occupancy grids for mobile robot preception and navigation”, IEEE Computer, June 1989, pp. 46–57.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1992

Authors and Affiliations

  • Amit Kumar Ray
    • 1
  • R. B. Misra
    • 1
  1. 1.Reliability Engineering CentreIndian Institute of TechnologyKharagpur - 721 302, WBIndia

Personalised recommendations