On-line prediction remaining useful life for ball bearings via grey NARX

Qiming Niu1 , Qingbin Tong2 , Junci Cao3 , Yihuang Zhang4 , Feng Liu5

1, 5School of Computer and Information Technology, Beijing Jiaotong University, Beijing, P. R. China

1Computing Center, Hebei University, BaoDing, P. R. China

2, 3, 4School of Electrical Engineering, Beijing Jiaotong University, Beijing, P. R. China

5Corresponding author

Journal of Vibroengineering, Vol. 21, Issue 1, 2019, p. 82-96. https://doi.org/10.21595/jve.2018.20120
Received 3 August 2018; received in revised form 18 September 2018; accepted 25 September 2018; published 15 February 2019

Copyright © 2019 Qiming Niu, et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Abstract.

The Huge vibration data are generated continuously by many sensors in daily high-speed rotating machinery operations. Accurate online prediction based on big vibration data streaming can reduce the risks related to failures and avoid service disruptions. This paper presents a hybrid nonlinear autoregressive network with exogenous inputs (NARX) model to forecast the remaining useful life of ball bearings through health index based on big vibration data streaming. This approach is validated by a real data from PRONOSTIA experimentation platform and industrial test rig compared with backpropagation neural network (BP), Elman and general regression neural network (GRNN) prediction model. Root mean square error, mean absolute error and correlation coefficient were used as performance indexes to evaluate the prediction accuracy of these models. The mean absolute error, the root mean square error and the correlation coefficient of hybrid NARX model evaluation index are 2.04, 2.85 and 0.98 respectively. It shows that the model presented in this paper has higher prediction accuracy. It can meet the needs of actual ball bearing remaining useful life prediction and also provide reference in other fields.

Keywords: health index, remaining useful life, ball bearings, prediction, data streaming, neural network.

Acknowledgements

This work was partially supported by Comprehensive standardization and new mode application project of Intelligent Manufacturing in Ministry of industry and information technology of People's Republic of China No. 2017ZNZZ01-06, the Science and Technology Research and Development Major/Key Program of China Railway Corporation No. 2016J007-B, National Natural Science Foundation of China No.51577007, and Beijing Natural Science Foundation (3162023). Finally, the authors are grateful to the anonymous reviewers for their helpful comments and constructive suggestions.

References

  1. Nair L. R., Shetty S. D., Shetty S. D. Applying spark based machine learning model on streaming big data for health status prediction. Computers and Electrical Engineering, Vol. 65, 2017, p. 393-399. [Publisher]
  2. Han D., Li S., Wei F., et al. Two birds with one stone: classifying positive and unlabeled examples on uncertain data streams. Neurocomputing, Vol. 277, 2017, p. 149-160. [Publisher]
  3. Fernández Rodríguez J.-Y., Álvarez García J.-A., Fisteus J. A., et al. Benchmarking real-time vehicle data streaming models for a smart city. Information Systems, Vol. 72, 2017, p. 62-76. [Publisher]
  4. Morales G. D. F., Bifet A. SAMOA: scalable advanced massive online analysis. Journal of Machine Learning Research, Vol. 16, 2015, p. 149-153. [CrossRef]
  5. Parker B. S., Khan L., Bifet A. Incremental ensemble classifier addressing non-stationary fast data streams. IEEE International Conference on Data Mining Workshops, 2014, p. 716-723. [CrossRef]
  6. Zeng X. Q., Li G. Z. Incremental partial least squares analysis of big streaming data. Pattern Recognition, Vol. 47, Issue 11, 2014, p. 3726-3735. [Publisher]
  7. Fumeo E., Oneto L., Anguita D. Condition based maintenance in railway transportation systems based on big data streaming analysis. Procedia Computer Science, Vol. 53, Issue 1, 2015, p. 437-446. [Publisher]
  8. Schoen R. R., Habetler T. G., Kamran F., et al. Motor bearing damage detection using stator current monitoring. IEEE Transactions on Industry Applications, Vol. 31, Issue 6, 1994, p. 1274-1279. [Publisher]
  9. Guo L., Li N., Jia F., et al. A recurrent neural network based health indicator for remaining useful life prediction of bearings. Neurocomputing, Vol. 240, 2017, p. 98-109. [Publisher]
  10. Wang Y., Peng Y., Zi Y., et al. A two-stage data-driven-based prognostic approach for bearing degradation problem. IEEE Transactions on Industrial Informatics, Vol. 12, Issue 3, 2016, p. 924-932. [Publisher]
  11. Zhao M., Tang B., Tan Q. Bearing remaining useful life estimation based on time-frequency representation and supervised dimensionality reduction. Measurement, Vol. 86, 2016, p. 41-55. [Publisher]
  12. Wu S., Gebraeel N., Lawley M. A., et al. System for condition-based optimal predictive maintenance policy. IEEE Transactions on Systems, Man, and Cybernetics, Vol. 37, 2007, p. 226-236. [Publisher]
  13. Gebraeel N., Lawley M., Liu R., et al. Residual life predictions from vibration-based degradation signals: a neural network approach. IEEE Transactions on Industrial Electronics, Vol. 51, Issue 3, 2004, p. 694-700. [Publisher]
  14. Wang F., Wang B., Dun B., et al. Remaining life prediction of rolling bearing based on PCA and improved logistic regression model. Journal of Vibroengineering, Vol. 18, Issue 8, 2016, p. 5192-5203. [Publisher]
  15. Rai A., Upadhyay S. H. The use of MD-CUMSUM and NARX neural network for anticipating the remaining useful life of bearings. Measurement, Vol. 111, 2017, p. 397-410. [Publisher]
  16. Zeng X. Y., Shu L., Huang G. M., et al. Triangular fuzzy series forecasting based on grey model and neural network. Applied Mathematical Modelling, Vol. 40, Issue 3, 2016, p. 1717-1727. [Publisher]
  17. Lei Y., Guo M., Hu D. D., et al. Short-term prediction of UT1-UTC by combination of the grey model and neural networks. Advances in Space Research, Vol. 59, Issue 2, 2017, p. 524-531. [Publisher]
  18. Liu X., Moreno B., Garcia A. S. A grey neural network and input-output combined forecasting model. Primary energy consumption forecasts in Spanish economic sectors. Energy, Vol. 115, 2016, p. 1042-1054. [CrossRef]
  19. Abdulshahed A. M., Longstaff A. P., Fletcher S., et al. Thermal error modelling of a gantry-type 5-axis machine tool using a grey neural network model. Journal of Manufacturing Systems, Vol. 41, 2016, p. 130-142. [Publisher]
  20. Rumelhart D. E., Hinton G. E., Williams R. J. Learning representations by back-propagating errors. Nature, Vol. 323, Issue 6088, 1986, p. 533-536. [Publisher]
  21. Specht D. F. A general regression neural network. IEEE Transactions on Neural Networks, Vol. 2, Issue 6, 1991, p. 568-576. [Publisher]
  22. Tse P. W., Atherton D. P. Prediction of machine deterioration using vibration based fault trends and recurrent neural networks. Journal of Vibration and Acoustics, Vol. 121, Issue 3, 1999, p. 355-362. [Publisher]
  23. Elman J. L. Finding structure in time. Cognitive Science, Vol. 14, Issue 2, 1990, p. 179-211. [Publisher]