A SURVEY OF CASE-BASED DIAGNOSTIC SYSTEMS FOR MACHINES

Erik Olsson

Abstract

Electrical and mechanical equipment such as gearboxes in an industrial robots or electronic circuits in an industrial printer sometimes fail to operate as intended. The faulty component can be hard to locate and replace and it might take a long time to get an enough experienced technician to the spot. In the meantime thousands of dollars may be lost due to a delayed production. Systems based on case-based reasoning are well suited to prevent this kind of hold in the production. Their ability to reason from past cases and to learn from new ones is a powerful method to use when a failure in a machine occurs. This enables a less experienced technician to use the proposed solution from the system and quickly repair the machine.

References

  1. Aamodt, A., Plaza, E. (1994). Case-based reasoning: Foundational issues, methodological variations, and system approaches. AI Communications 7, pages 39- 59.
  2. Aha, D.W., Kibler, D., Albert, M. (1991) . Instance based learning algorithms. Machine Learning. 6, pages 37- 66.
  3. Bachman, G., (2000). Fourier and Wavelet Analysis. Springer, New York.
  4. Carpenter, G. A., Grossberg, S. (1988). The art of adaptive pattern recognition by a self-organizing neural network. IEEE Transactions on Computer 21, pages 77-88.
  5. Kohonen, T. (1995). Self-organizing maps. New York: Springer-Verlag.
  6. Olsson, E., Funk, P., Xiong N. (2004). Fault Diagnosis in Industry Using Case-Based Reasoning. Journal of Intelligent & Fuzzy Systems, Vol. 15.
  7. Penta, K.K. Khemani, D. (2004). Satelllite Health Monitoring using CBR Framework. Advances in Case-Based Reasoning. Seventh European Conference, pages 732-747.
  8. Pous, C., Colomer, J. Melendez, J. (2004). Extending a Fault Dictionary Towards a Case Based Reasoning System for Linear Electronic Analog Circuits Diagnosis. Advances in Case-Based Reasoning. Seventh European Conference, pages 748-762.
  9. Varma, A. (1999). ICARUS: Design and Deployment of a Case-Based Reasoning System for Locomotive Diagnostics. Case-Based Reasoning Research and Development: Third International Conference on Case-Based Reasoning, ICCBR-99, Proceedings, pages 581-596.
  10. Wilson, D., Martinez, T. (2000). Reduction Techniques for Instance-based Learning Algorithms.
  11. Machine Learning 38, pages 257-286.
  12. Yang, B., Han, T., Kim, Y. (2004). Integration of ARTKohonen neural network and case-based reasoning for intelligent fault diagnosis. Expert Systems with Applications 26, pages 387-395.
Download


Paper Citation


in Harvard Style

Olsson E. (2005). A SURVEY OF CASE-BASED DIAGNOSTIC SYSTEMS FOR MACHINES . In Proceedings of the Seventh International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 972-8865-19-8, pages 381-385. DOI: 10.5220/0002522003810385


in Bibtex Style

@conference{iceis05,
author={Erik Olsson},
title={A SURVEY OF CASE-BASED DIAGNOSTIC SYSTEMS FOR MACHINES},
booktitle={Proceedings of the Seventh International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2005},
pages={381-385},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002522003810385},
isbn={972-8865-19-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Seventh International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - A SURVEY OF CASE-BASED DIAGNOSTIC SYSTEMS FOR MACHINES
SN - 972-8865-19-8
AU - Olsson E.
PY - 2005
SP - 381
EP - 385
DO - 10.5220/0002522003810385