Journal of Intelligent Manufacturing

, Volume 23, Issue 2, pp 227–237 | Cite as

An artificial neural network method for remaining useful life prediction of equipment subject to condition monitoring

  • Zhigang Tian


Accurate equipment remaining useful life prediction is critical to effective condition based maintenance for improving reliability and reducing overall maintenance cost. In this paper, an artificial neural network (ANN) based method is developed for achieving more accurate remaining useful life prediction of equipment subject to condition monitoring. The ANN model takes the age and multiple condition monitoring measurement values at the present and previous inspection points as the inputs, and the life percentage as the output. A function generalized from the Weibull failure rate function is used to fit each condition monitoring measurement series for a failure history, and the fitted measurement values are used to form the ANN training set so as to reduce the effects of the noise factors that are irrelevant to the equipment degradation. A validation mechanism is introduced in the ANN training process to improve the prediction performance of the ANN model. The proposed ANN method is validated using real-world vibration monitoring data collected from pump bearings in the field. A comparative study is performed between the proposed ANN method and an adapted version of a reported method, and the results demonstrate the advantage of the proposed method in achieving more accurate remaining useful life prediction.


Remaining useful life Prediction Artificial neural network Accurate Bearing 


  1. Banjevic D., Jardine A. K. S., Makis V. (2001) A control-limit policy and software for condition-based maintenance optimization. INFOR 39: 32–50Google Scholar
  2. Dong M., He D. (2007a) A segmental hidden semi-Markov model (HSMM)-based diagnostics and prognostics framework and methodology. Mechanical Systems and Signal Processing 21(5): 2248–2266CrossRefGoogle Scholar
  3. Dong M., He D. (2007b) Hidden semi-Markov model-based methodology for multi-sensor equipment health diagnosis and prognosis. European Journal of Operational Research 178(3): 858–878CrossRefGoogle Scholar
  4. Dong M., He D., Banerjee P., Keller J. (2006) Equipment health diagnosis and prognosis using hidden semi-Markov models. International Journal of Advanced Manufacturing Technology 30(7–8): 738–749CrossRefGoogle Scholar
  5. Gebraeel N., Lawley M. A. (2008) A neural network degradation model for computing and updating residual life distributions. IEEE Transactions on Automation Science and Engineering 5(1): 154–163CrossRefGoogle Scholar
  6. Gebraeel N., Lawley M. A., Liu R. (2004) Residual life predictions from vibration-based degradation signals: A neural network approach. IEEE Transactions on Industrial Electronics 51(3): 694–700CrossRefGoogle Scholar
  7. Huang R. Q., Xi L. F., Li X. L., Liu C. R., Qiu H., Lee J. (2007) Residual life predictions for ball bearings based on self-organizing map and back propagation neural network methods. Mechanical Systems and Signal Processing 21(1): 193–207CrossRefGoogle Scholar
  8. Inman D. J., Farrar C. R., Lopes V. (2005) Damage prognosis: For aerospace, civil and mechanical systems. Wiley, NYCrossRefGoogle Scholar
  9. Jardine A. K. S., Lin D. M., Banjevic D. (2006) A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing 20(7): 1483–1510CrossRefGoogle Scholar
  10. Kacprzynski, G. J., Roemer, M. J., Modgil, G., Palladino, A., & Maynard, K. (2002). Enhancement of physics-of-failure prognostic models with system level features. In Proceedings of the 2002 IEEE aerospace conference, Big Sky, MT, USA.Google Scholar
  11. Kuo W., Zuo M. J. (2003) Optimal reliability modeling: Principles and applications. Wiley, HobokenGoogle Scholar
  12. Lee J., Ni J., Djurdjanovic D., Qiu H., Liao L. T. (2006) Intelligent prognostics tools and e-maintenance. Computers in Industry 57: 476–489CrossRefGoogle Scholar
  13. Levitin G. (2005) Universal generating function in reliability analysis and optimization. Springer-Verlag, LondonGoogle Scholar
  14. Levitin G., Lisnianski A., Ben Haim H., Elmakis D. (1998) Redundancy optimization for series-parallel multistate systems. IEEE Transactions on Reliability 47(2): 165–172CrossRefGoogle Scholar
  15. Li C. J., Lee H. (2005) Gear fatigue crack prognosis using embedded model, gear dynamic model and fracture mechanics. Mechanical Systems and Signal Processing 19: 836–846CrossRefGoogle Scholar
  16. Liao H. T., Elsayed E. A., Chan L. Y. (2006) Maintenance of continuously monitored degrading systems. European Journal of Operational Research 175(2): 821–835CrossRefGoogle Scholar
  17. Lin D., Banjevic D., Jardine A. K. S. (2006) Using principal components in a proportional hazards model with applications in condition-based maintenance. Journal of the Operational Research Society 57: 910–919CrossRefGoogle Scholar
  18. Makis V., Jardine A. K. S. (1992) Optimal replacement in the proportional hazards model. INFOR 30: 172–183Google Scholar
  19. Marble, S., & Morton, B. P. (2006). Predicting the remaining life of propulsion system bearings. In Proceedings of the 2006 IEEE aerospace conference, Big Sky, MT, USA.Google Scholar
  20. Rojas R. (1996) Neural networks: A system introduction. Springer, BerlinGoogle Scholar
  21. Stevens, B. (2006). EXAKT reduces failures at Canadian Kraft Mill.
  22. Tian, Z., & Zuo, M. J. (2009). Health condition prognostics of gears using a recurrent neural network approach. In Proceedings of the reliability and maintainability symposium, Fort Worth, TX, USA.Google Scholar
  23. Tse P. W., Atherton D. P. (1999) Prediction of machine deterioration using vibration based fault trends and recurrent neural networks. Journal of Vibration and Acoustics, Transactions of ASME 121: 355–362CrossRefGoogle Scholar
  24. Vachtsevanos G., Lewis F. L., Roemer M., Hess A., Wu B. (2006) Intelligent fault diagnosis and prognosis for engineering systems. Wiley, NYCrossRefGoogle Scholar
  25. Wu S. J., Gebraeel N., Lawley M. A., Yih Y. (2007) A neural network integrated decision support system for condition-based optimal predictive maintenance policy. IEEE Transactions on Systems Man and Cybernetics Part A: Systems and Humans 37(2): 226–236CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Concordia Institute for Information Systems EngineeringConcordia UniversityMontrealCanada

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