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A case study of remaining storage life prediction using stochastic filtering with the influence of condition monitoring

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  • Wang, Zhaoqiang
  • Hu, Changhua
  • Wang, Wenbin
  • Zhou, Zhijie
  • Si, Xiaosheng

Abstract

Some systems may spend most of their time in storage, but once needed, must be fully functional. Slow degradation occurs when the system is in storage, so to ensure the functionality of these systems, condition monitoring is usually conducted periodically to check the condition of the system. However, taking the condition monitoring data may require putting the system under real testing situation which may accelerate the degradation, and therefore, shorten the storage life of the system. This paper presents a case study of condition-based remaining storage life prediction for gyros in the inertial navigation system on the basis of the condition monitoring data and the influence of the condition monitoring data taking process. A stochastic-filtering-based degradation model is developed to incorporate both into the prediction of the remaining storage life distribution. This makes the predicted remaining storage life depend on not only the condition monitoring data but also the testing process of taking the condition monitoring data, which the existing prognostic techniques and algorithms did not consider. The presented model is fitted to the real condition monitoring data of gyros testing using the maximum likelihood estimation method for parameter estimation. Comparisons are made with the model without considering the process of taking the condition monitoring data, and the results clearly demonstrate the superiority of the newly proposed model.

Suggested Citation

  • Wang, Zhaoqiang & Hu, Changhua & Wang, Wenbin & Zhou, Zhijie & Si, Xiaosheng, 2014. "A case study of remaining storage life prediction using stochastic filtering with the influence of condition monitoring," Reliability Engineering and System Safety, Elsevier, vol. 132(C), pages 186-195.
    • Handle: RePEc:eee:reensy:v:132:y:2014:i:c:p:186-195
    DOI: 10.1016/j.ress.2014.07.015
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    References listed on IDEAS

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    1. W Wang, 2011. "Overview of a semi-stochastic filtering approach for residual life estimation with applications in condition based maintenance," Journal of Risk and Reliability, , vol. 225(2), pages 185-197, June.
    2. Wang, Wenbin & Hussin, B. & Jefferis, Tim, 2012. "A case study of condition based maintenance modelling based upon the oil analysis data of marine diesel engines using stochastic filtering," International Journal of Production Economics, Elsevier, vol. 136(1), pages 84-92.
    3. Herzog, M.A. & Marwala, T. & Heyns, P.S., 2009. "Machine and component residual life estimation through the application of neural networks," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 479-489.
    4. W Wang & A H Christer, 2000. "Towards a general condition based maintenance model for a stochastic dynamic system," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 51(2), pages 145-155, February.
    5. Jin, Guang & Matthews, David E. & Zhou, Zhongbao, 2013. "A Bayesian framework for on-line degradation assessment and residual life prediction of secondary batteries inspacecraft," Reliability Engineering and System Safety, Elsevier, vol. 113(C), pages 7-20.
    6. Zio, Enrico & Peloni, Giovanni, 2011. "Particle filtering prognostic estimation of the remaining useful life of nonlinear components," Reliability Engineering and System Safety, Elsevier, vol. 96(3), pages 403-409.
    7. Si, Xiao-Sheng & Wang, Wenbin & Hu, Chang-Hua & Zhou, Dong-Hua, 2011. "Remaining useful life estimation - A review on the statistical data driven approaches," European Journal of Operational Research, Elsevier, vol. 213(1), pages 1-14, August.
    8. Jiang, R., 2008. "A Gamma–normal series truncation approximation for computing the Weibull renewal function," Reliability Engineering and System Safety, Elsevier, vol. 93(4), pages 616-626.
    9. M J Carr & W Wang, 2008. "A case comparison of a proportional hazards model and a stochastic filter for condition-based maintenance applications using oil-based condition monitoring information," Journal of Risk and Reliability, , vol. 222(1), pages 47-55, March.
    10. Zio, Enrico & Di Maio, Francesco, 2010. "A data-driven fuzzy approach for predicting the remaining useful life in dynamic failure scenarios of a nuclear system," Reliability Engineering and System Safety, Elsevier, vol. 95(1), pages 49-57.
    11. Hu, Chao & Youn, Byeng D. & Wang, Pingfeng & Taek Yoon, Joung, 2012. "Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life," Reliability Engineering and System Safety, Elsevier, vol. 103(C), pages 120-135.
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    2. Cheng, Yao & Elsayed, Elsayed A., 2018. "Reliability modeling and optimization of operational use of one-shot units," Reliability Engineering and System Safety, Elsevier, vol. 176(C), pages 27-36.
    3. Zhang, Yongjin & Zhao, Ming & Zhang, Shitao & Wang, Jiamei & Zhang, Yanjun, 2017. "An integrated approach to estimate storage reliability with initial failures based on E-Bayesian estimates," Reliability Engineering and System Safety, Elsevier, vol. 159(C), pages 24-36.
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    6. Zhang, Zhengxin & Si, Xiaosheng & Hu, Changhua & Lei, Yaguo, 2018. "Degradation data analysis and remaining useful life estimation: A review on Wiener-process-based methods," European Journal of Operational Research, Elsevier, vol. 271(3), pages 775-796.

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