Skip to main content
SpringerLink
Log in

Feature signature prediction of a boring process using neural network modeling with confidence bounds

  • Original Article
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

Prediction of machine tool failure has been very important in modern metal cutting operations in order to meet the growing demand for product quality and cost reduction. This paper presents the study of building a neural network model for predicting the behavior of a boring process during its full life cycle. This prediction is achieved by the fusion of the predictions of three principal components extracted as features from the joint time–frequency distributions of energy of the spindle loads observed during the boring process. Furthermore, prediction uncertainty is assessed using nonlinear regression in order to quantify the errors associated with the prediction. The results show that the implemented Elman recurrent neural network is a viable method for the prediction of the feature behavior of the boring process, and that the constructed confidence bounds provide information crucial for subsequent maintenance decision making based on the predicted cutting tool degradation.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Djurdjanovic D, Ni J, Lee J (2002) Time–frequency based sensor fusion in the assessment and monitoring of machine performance degradation. ASME DSC Division 71:15–22

    Google Scholar 

  2. NSF I/UCRC Center for Intelligent Maintenance Systems (2002) Home page at: http://www.imscenter.net/

  3. Lee J (1995) Machine performance monitoring and proactive maintenance in computer-integrated manufacturing: review and perspective. Int J Comput Integ Manuf 8:370–380

    Article  Google Scholar 

  4. Lee J (1996) Measurement of machine performance degradation using a neural network model. Comput Ind 30:193–209

    Article  Google Scholar 

  5. Engel SJ, Gilmartin BJ, Bongort K, Hess A (2000) Prognostics, the real issues involved with predicting life remaining. In: Proceedings of the IEEE Aerospace Conference, Big Sky, Montana, March 2000, vol 6, pp 457–469

  6. Yang Z, Djurdjanovic D, Mayor R, Ni J, Lee J (2004) Maintenance scheduling in production systems based on predicted machine degradation. Automat Sci Eng (submitted, paper no V2004–067)

  7. Zhang SF, Liu RJ (2000) A rapid algorithm for on-line and real-time ARMA modeling. In: Proceedings of the 5th International Conference on Signal Processing (WCCC-ICSP 2000), Beijing, China, August 2000, pp 230–233

  8. Weigend AS, Huberman BA, Rumelhart DE (1990) Predicting the future: a connectionist approach. Int J Neural Syst 1:193–209

    Article  Google Scholar 

  9. Rape R, Fefer D, Jeglic (1995) Comparison of neural networks to statistical techniques for prediction of time series generated by nonlinear dynamic systems. In: Proceedings of the Instrumentation and Measurement Technology Conference (IMTC/95), Waltham, Massachusetts, April 1995, pp 300–304

  10. Lowe D, Zapart C (1999) Point-wise confidence interval estimation by neural networks: a comparative study based on automotive engine calibration. Neural Comput Appl 8:77–85

    Article  Google Scholar 

  11. De Veaux RD, Schumi J, Schweinsberg J, Unger LH (1998) Prediction intervals for neural networks via nonlinear regression. Technometrics 40:273–282

    Article  MathSciNet  MATH  Google Scholar 

  12. Efron B, Tibshirani RJ (1993) An introduction to the bootstrap. Chapman and Hall, New York

    MATH  Google Scholar 

  13. Huang JTG, Ding AA (1997) Prediction intervals for artificial neural networks. J Am Stat Assoc 92:748–757

    Article  Google Scholar 

  14. Lapedes A, Farber R (1987) Nonlinear signal processing using neural networks: prediction and system modeling. Los Alamos National Laboratory Report, Los Alamos, NM, technical report no. LA-UR-87-2662

  15. Lee KY, Choi TI, Ku CC, Park JH (1994) Neural network architectures for short-term load forecasting. In: Neural Networks, proceedings of the IEEE World Congress on Computational Intelligence, Orlando, Florida, June/July 1994, pp 4724–4729

  16. Senjyu T, Takara H, Uezato K, Funabashi T (2002) One-hour-ahead load forecasting using neural network. IEEE T Power Syst 17:113–118

    Article  Google Scholar 

  17. Wulff NH, Hertz JA (1992) Prediction with recurrent networks. In: Proceedings of the 1992 IEEE-SP Workshop on Neural Networks for Signal Processing, Copenhagen, Denmark, August/September 1992, pp 464–473

  18. Khotanzad A, Abaye A, Maratukulam D (1994) An adaptive recurrent neural network system for multi-step-ahead hourly prediction of power system loads. In: Neural Networks, proceedings of the IEEE World Congress on Computational Intelligence, Orlando, Florida, June/July 1994, pp 3393–3397

  19. Logar AM, Corwin EM, Oldham WJB (1993) A comparison of recurrent neural network learning algorithms. In: Proceedings of the IEEE International Conference on Neural Networks, San Francisco, California, March 1993, pp 1129–1134

  20. Rao SS, Sethuraman S, Ramamurti V (1992) A recurrent neural network for nonlinear time series prediction–a comparative study. In: Proceedings of the 1992 IEEE-SP Workshop on Neural Networks for Signal Processing, Copenhagen, Denmark, August/September 1992, pp 531–539

  21. Chen CH, Yu L (1997) A learning algorithm for improved recurrent neural networks. In: Proceedings of the International Conference on Neural Networks (ICNN’97), Houston, Texas, June 1997, pp 2198–2202

  22. Connor J, Atlas L (1991) Recurrent neural networks and time series prediction. In: Proceedings of the Seattle International Joint Conference on Neural Networks (IJCNN’91–Seattle), Seattle, Washington, July 1991, pp 301–306

  23. Jenq-Neng Hwang, Little E (1996) Real time recurrent neural networks for time series prediction and confidence estimation. In: Proceedings of the IEEE International Conference on Neural Networks (ICNN’96), Washington, DC, June 1996, pp 1889–1894

  24. Barton RS, Himmelblau DM (1997) Online prediction of polymer product quality in an industrial reactor using recurrent neural networks. In: Proceedings of the International Conference on Neural Networks (ICNN’97), Houston, Texas, June 1997, pp 111–114

  25. Goh WY, Lim CP, Peh KK, Subari K (2000) A neural-network-based intelligent system for time-series prediction problems in product development. In: Proceedings of TENCON 2000, Kuala Lumpur, Malaysia, September 2000, pp 151–155

  26. Jun Zhang, Tang KS, Man KF (1997) Recurrent NN model for chaotic time series prediction. In: Proceedings of the 23rd International Conference on Industrial Electronics, Control and Instrumentation (IECON’97), New Orleans, Louisiana, November 1997, pp 1108–1112

  27. Zhang J, Man KF (1998) Time series prediction using RNN in multi-dimension embedding phase space. In: Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC), San Diego, California, October 1998, pp 1868–1873

  28. Seber GAF, Wild CJ (1989) Nonlinear regression. Wiley, New York

    MATH  Google Scholar 

  29. Cohen L (1995) Time-frequency analysis. Prentice-Hall, Englewood Cliffs, New Jersey

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering and Automation, Harbin Institute of Technology (HIT) Shenzhen Graduate School, Xili Shenzhen University Town HIT Campus, Shenzhen, Guangdong, 518055, P.R. China

    Gang Yu

  2. Department of Industrial and Manufacturing Engineering, University of Wisconsin-Milwaukee, Milwaukee, WI, 53224, USA

    Hai Qiu & Jay Lee

  3. Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA

    Dragan Djurdjanovic

Authors
  1. Gang Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hai Qiu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dragan Djurdjanovic
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jay Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gang Yu.

Additional information

NSF Industry/University Cooperative Research Center (NSF I/UCRC) forIntelligent Maintenance Systems(IMS).

Rights and permissions

Reprints and permissions

About this article

Cite this article

Yu, G., Qiu, H., Djurdjanovic, D. et al. Feature signature prediction of a boring process using neural network modeling with confidence bounds. Int J Adv Manuf Technol 30, 614–621 (2006). https://doi.org/10.1007/s00170-005-0114-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-005-0114-x

Keywords

Search

Navigation