Skip to main content
SpringerLink
Log in

Residual life prediction based on dynamic weighted Markov model and particle filtering

  • Published:
Journal of Intelligent Manufacturing Aims and scope Submit manuscript

Abstract

In order to improve the prediction accuracy of non-Gaussian data and build reasonably the prediction model, a novel residual life prediction method is proposed. A dynamic weighted Markov model is constructed by real time data and historical data, and the residual life is predicted by particle filter. The particles of the state vector are predicted and updated instantaneously using particle filter. The probability distribution of the predicted value is estimated by the updated particles. The residual life can be predicted using the set threshold of the state. This method improves the accuracy of residual life prediction. Finally, the advantage of this proposed method was shown experimentally using the bearings’ full cycle data.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  • Baraldi, P., Compare, M., Sauco, S., et al. (2013). Ensemble neural network-based particle filtering for prognostics. Mechanical Systems and Signal Processing, 41(1), 288–300.

    Article  Google Scholar 

  • Çaydaş, U., & Ekici, S. (2012). Support vector machines models for surface roughness prediction in CNC turning of AISI 304 austenitic stainless steel. Journal of Intelligent Manufacturing, 23(3), 639–650.

    Article  Google Scholar 

  • Chao, P. Y., & Hwang, Y. D. (1997). An improved neural network model for the prediction of cutting tool life. Journal of Intelligent Manufacturing, 8(2), 107–115.

    Article  Google Scholar 

  • Chen, X., Shen, Z., He, Z., et al. (2013). Residual life prognostics of rolling bearing based on relative features and multivariable support vector machine. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 227(12), 2849–2860.

    Google Scholar 

  • Chen, C., Vachtsevanos, G., & Orchard, M. E. (2012). Machine residual useful life prediction: An integrated adaptive neuro-fuzzy and high-order particle filtering approach. Mechanical Systems and Signal Processing, 28, 597–607.

    Article  Google Scholar 

  • Gebraeel, N. Z., Lawley, M. A., Li, R., et al. (2005). Residual-life distributions from component degradation signals: A Bayesian approach. IIE Transactions, 37(6), 543–557.

    Article  Google Scholar 

  • Hartigan, J. A., & Wong, M. A. (1979). Algorithm AS 136: A k-means clustering algorithm. Applied Statistics, 100–108.

  • Kharoufeh, J. P., & Cox, S. M. (2005). Stochastic models for degradation-based reliability. IIE Transactions, 37(6), 533–542.

    Article  Google Scholar 

  • Kharoufeh, J. P., & Mixon, D. G. (2009). On a Markovc—Modulated shock and wear process. Naval Research Logistics (NRL), 56(6), 563–576.

    Article  Google Scholar 

  • Kimotho, J. K., Sondermann-Woelke, C., Meyer, T., et al. (2013). Machinery prognostic method based on multi-class support vector machines and hybrid differential evolution-particle swarm optimization. Chemical Engineering Transactions, 33, 619–624.

    Google Scholar 

  • Liu, Q., Dong, M., & Peng, Y. (2012). A novel method for online health prognosis of equipment based on hidden semi-Markov model using sequential Monte Carlo methods. Mechanical Systems and Signal Processing, 32, 331–348.

    Article  Google Scholar 

  • Ng, S. S. Y., Xing, Y., & Tsui, K. L. (2014). A naive Bayes model for robust residual useful life prediction of lithium-ion battery. Applied Energy, 118, 114–123.

  • Orchard, M. E. (2007). A particle filtering-based framework for on-line fault diagnosis and failure prognosis. Atlanta: Georgia Institute of Technology.

    Google Scholar 

  • Pitt, M., Silva, R., Giordani, P., et al. (2010). Auxiliary particle filtering within adaptive Metropolis–Hastings sampling. arXiv preprint arXiv:1006.1914.

  • Tian, Z. (2012). An artificial neural network method for remaining useful life prediction of equipment subject to condition monitoring. Journal of Intelligent Manufacturing, 23(2), 227–237.

    Article  Google Scholar 

  • Tobon-Mejia, D. A., Medjaher, K., & Zerhouni, N. (2012). CNC machine tool’s wear diagnostic and prognostic by using dynamic Bayesian networks. Mechanical Systems and Signal Processing, 28, 167–182.

    Article  Google Scholar 

  • Wang, Y., Deng, C., Wu, J., et al. (2013). Failure time prediction for mechanical device based on the degradation sequence. Journal of Intelligent Manufacturing, 13, 1–19.

  • Yan, J., Guo, C., & Wang, X. (2011). A dynamic multi-scale Markov model based methodology for residual life prediction. Mechanical Systems and Signal Processing, 25(4), 1364–1376.

    Article  Google Scholar 

  • Yaqub, M. F., Gondal, I., & Kamruzzaman, J. (2013). Multi-step support vector regression and optimally parameterized wavelet packet transform for machine residual life prediction. Journal of Vibration and Control, 19(7), 963–974.

    Article  Google Scholar 

Download references

Acknowledgments

The work described in this paper was supported by a Grant from the national defence researching fund (No. 9140A27020413JB11076).

Author information

Authors and Affiliations

  1. Naval University of Engineering Power Engineering Marine Engineering, WuHan, 430033, China

    Shuai Zhang, Yongxiang Zhang & Jieping Zhu

Authors
  1. Shuai Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yongxiang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jieping Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yongxiang Zhang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, S., Zhang, Y. & Zhu, J. Residual life prediction based on dynamic weighted Markov model and particle filtering. J Intell Manuf 29, 753–761 (2018). https://doi.org/10.1007/s10845-015-1127-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10845-015-1127-4

Keywords

Search

Navigation