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

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. 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. Devijver, P.A. and Kittler,J., Patteren Recognition: A statistical Approach, Printice Hall, 1982.

   Google Scholar 

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. 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.

   Article  Google Scholar 

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. 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. 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. Elfes, A., 1989, “Using occupancy grids for mobile robot preception and navigation”, IEEE Computer, June 1989, pp. 46–57.

   Google Scholar 

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