Abstract
Automated assembly systems often stop their operation due to the unexpected failures occurred during their assembly process. Since these large-scale systems are composed of many parameters, it is difficult to anticipate all possible types of errors with their likelihood of occurrence. Several systems were developed in the literature, focussing on on-line diagnosing and recovery of the assembly process in an intelligent manner based on the predicted error scenarios. However, these systems do not cover all of the possible errors and they are deficient in dealing with the unexpected error situations. The proposed approach uses Monte Carlo simulation of the assembly process with the 3-D model of the assembly line to predict the possible errors in an off-line manner. After that, these predicted errors are diagnosed and recovered using Bayesian reasoning and genetic algorithms. Several case studies are performed on single-station and multi-station assembly systems and the results are discussed. It is expected that with this new approach, errors can be diagnosed and recovered accurately and costly downtimes of robotic assembly systems will be reduced.
Similar content being viewed by others
References
Abu-Hamdan, M. G. and El-Gizawy, A. S. (1997) Computer aided monitoring system for flexible assembly operation. Computers in Industry, 34, 1–10.
Baydar, C. and Saitou, K. (2001a) Automated generation of error recovery logic in assembly systems using genetic programming. Journal of Manufacturing Systems, 20(1), 55–68.
Baydar, C. and Saitou, K. (2001b) Off-line error prediction, diagnosis and recovery using virtual assembly systems. Proceedings of the 2001 IEEE International Conference on Robotics and Automation.
Baydar, C. and Saitou, K. (2001c) Prediction and diagnosis of propagated failures in assembly systems using virtual factories. Proceedings ASME Design Engineering Technical Conferences—Computers in Engineering.
Cao, T. C. and Sanderson, A. C. (1992) Sensor-based error recovery for robotic task sequences using fuzzy petrinets. Proceedings of the 1992 IEEE International Conference on Robotics and Automation, 2, 1063–1069.
Chang, S. J., DiCesare, F. and Goldbogen, G. (1991) Failure propagation trees for diagnosis in manufacturing systems. IEEE Transactions on System Man Cybernetics, 21(4), 767–776.
ElMaraghy, H. A., ElMaraghy, W. H. and Knoll, L. (1988) Design specification of parts dimensional tolerance for robotic assembly. Computers in Industry, 10, 47–59.
Evans, E. Z. and Lee, S. G. (1994) Automatic generation of error recovery knowledge through learned activity. Proceedings of the 1994 IEEE International Conference on Robotics and Automation, 4, 2915–2920.
Jennings, J., Donald, B. and Campbell, D. (1989) Towards experimental verification of an automated compliant motion planner based on a geometric theory of error detection and recovery. Proceedings of the IEEE International Conference on Robotics and Automation, 632–637.
Jing, Q., Xisen, W., Zhihua, P. and Youngcheng, X. (1996) A research on fault diagnostic expert system based on fuzzy petri nets for FMS machining cell. Proceedings IEEE International Conference on Industrial Technology, 122–125.
Kang, L. and Wenhan, Q. (1993) Fuzzy expert system in robotic assembly workcell. Proceedings IEEE TENCON, 738–741.
Koza, J. R. (1992) Genetic Programming: On the Programming of Computers by Natural Selection, MIT Press, Cambridge, MA.
Lam, R. K., Pollard, N. S. and Desai, R. S. (1990) Studies in knowledge-based diagnosis of failures in robotic assembly. Proceedings of the IEEE Conference on Robotics and Automation, 60–65.
Lopes, L. S. and Camarinho-Matos, L. M. (1996) Towards intelligent execution supervision for flexible assembly systems. Proceedings of the IEEE International Conference on Robotics and Automation, 1225–1230.
Lunze, J. and Schiller, F. (1999) An example of fault diagnosis by means of probabilistic logic reasoning. Control Engineering Practice, 7, 271–278.
Luxhoj, J. T., Riis, J. O. and Thorsteinsson, U. (1997) Trends and perspectives in industrial maintenance management. Journal of Manufacturing Systems, 16(6).
Sampath, M., Sengupta, R., Lafortune, S., Sinnamohideen, K. and Teneketzis, C. (1996) Failure diagnosis using discrete-event models. IEEE Transactions on Control Systems Technology, 4(2), 105–124.
Srinivas, S. (1997) Error Recovery in Robot Systems. Ph.D. Thesis, California Institute of Technology.
Tzafestas, S. and Stamou, G. B. (1997) Concerning automated assembly: knowledge-based issues and a fuzzy system for assembly under uncertainty. Computer Integrated Manufacturing Systems, 10(3), 183–192.
Visinsky, M. L., Cavallaro, J. R. and Walker, I. D. (1994) Expert system framework for fault detection and fault tolerance in robotics. Computers in Electrical Engineering, 20(5), 421–435.
Workspace 5 User-Manual. (2000) Flow Software, Inc.
Workspace 4 User-Manual. (1998) Flow Software, Inc.
Zhou, M. C. and DiCesare, F. (1989) Adaptive design of petri-net controllers for error recovery in automated manufacturing systems. IEEE Transactions on Systems, Man and Cybernetics, 19(5), 963–973.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Baydar, C., Saitou, K. Off-line error prediction, diagnosis and recovery using virtual assembly systems. Journal of Intelligent Manufacturing 15, 679–692 (2004). https://doi.org/10.1023/B:JIMS.0000037716.69868.d0
Issue Date:
DOI: https://doi.org/10.1023/B:JIMS.0000037716.69868.d0