한국공작기계학회논문집 (Transactions of the Korean Society of Machine Tool Engineers)

한국생산제조학회 (The Korean Society of Manufacturing Technology Engineers)
권호 표지

ART2 신경회로망을 이용한 공작기계의 웹기반 원격 성능저하 모니터링 시스템 개발

Development of a Web-Based Remote Monitoring System for Evaluating Degradation of Machine Tools Using ART2

  • 김초원 (국립창원대학교 기계설계공학과) ;
  • 최국진 (국립창원대학교 기계설계공학과) ;
  • 정성환 (국립창원대학교 컴퓨터공학과) ;
  • 홍대선 (국립창원대학교 메카트로닉스공학부)
  • 발행 : 2009.02.15
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

This study proposes a web-based remote monitoring system for evaluating degradation of machine tools using ART2(Adaptive Resonance Theory 2) neural network. A number of studies on the monitoring of machine tools using neural networks have been reported. However, when normal condition is changed due to factors such as maintenance, tool change etc., or a new failure signal is generated, such algorithms need to be entirely retrained in order to accommodate the new signals. To cope with such problems, this study develops a remote monitoring system using ART2 in which new signals when required are simply added to the classes previously trained. This system can monitor degradation as well as failure of machine tools. To show the effectiveness of the proposed approach, the system is experimentally applied to monitoring a simulator similar to the main spindle of a machine tool, and the results show that the proposed system can be extended to monitoring of real industrial machine tools and equipment.

키워드

참고문헌

  1. Peng, Z. and Kessissoglou, N., 2003, "An Integrated Approach to Fault Diagnosis of Machinery Using Wear Debris and Vibration Analysis," International Conference on Wear of Materials, Vol. 255, No. 7-12, pp. 1221-1232.
  2. He, Z., Zi, Y., and Chen, P., 2003, "Predictive Equipment Maintenance Based on Information Extracted form its Running Condition," International Conference on Intelligent Maintenance System, pp. 38-44.
  3. Min, H. S., Lee, S. H., and Ahn, S. E., 2002, "Monitoring of Laser Fusion Cutting Using Acoustic Emission," Transactions of the Korean Society of Machine Tool Engineers, Vol. 11, No. 3, pp. 39-44.
  4. Lucking, W. G., Darnell, G., and Chesmore, E. D., 1994, "Acoustical Condition Monitoring of a Mechanical Gearbox Using Artificial Neural Networks," IEEE International Conference on Neural Networks, Vol. 5, pp. 3307-3311.
  5. Prasopchaichana, K., and Kwon, O. Y., 2008, "Sensor Fusion and Neural Network Analysis for Drill-Wear Monitoring," Transactions of the Korean Society of Machine Tool Engineers, Vol. 17, No. 1, pp. 77-85.
  6. Carpenter, G. A. and Grossberg, S., 1987, "ART 2: Self-Organization of Stable Category Recognition Codes for Analog Input Patterns," Applied Optics, Vol. 26, No. 23, pp. 4919-4930. https://doi.org/10.1364/AO.26.004919
  7. Kohonen, T., 1995, Self-Organizing Maps, Springer-Verlag, New York.
  8. Kohonen, T., Oja, E., Simula, O., Visa, A. and Kangas, J., 1996, "Engineering Application of the Self-Organizing Map," Proceedings of the IEEE, Vol. 84, No. 10, pp. 1358-1384. https://doi.org/10.1109/5.537105
  9. Kim, S. H., Kim, D. H., and Lee, Y. S., 2004, "Study On the Design of Risk Management Web-Monitoring System using AANN," Journal of Control, Automation and Systems Engineering, Vol. 10, No. 6, pp. 545-550. https://doi.org/10.5302/J.ICROS.2004.10.6.545
  10. Toshiyuki, A., Takashi, K. B. X., and Shoji, H., 1999, "Fault Diagnosis System for Machines Using Neural Networks," Transactions of the Japan Society of Mechanical Engineers, Vol. 65, No. 632, pp. 1498-1504.
  11. Kim, S. H., Kim, D. H., Han, G. S., and Kim, C. B., 2002, "Model of Remote Service and Intelligent Fault Diagnosis for CNC Machine Tool," Journal of the Korean Society of Precision Engineering, Vol. 19, No. 4, pp. 168-178.
  12. National Instruments Corporation, n.d., viewed 1 July 2008, .
  13. The Apache Software Foundation, n.d., viewed 1 July 2008, .
  14. The PHP Group, n.d., viewed 1 August 2008, .
  15. Sun Microsystems, Inc., n.d., viewed 1 August 2008, .
  16. The MathWorks, Inc., n.d., viewed 1 August 2008, .