Prediction method for remaining useful life of gearbox based on kernel estimation and stochastic filtering theory

被引：0
|
作者：
Shi H. [1 ]
Song R. [1 ]
Zhang Y. [1 ]
Dong Z. [1 ]
机构：
[1] School of Electronic and Information Engineering, Taiyuan University of Science and Technology, Taiyuan
来源：
Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS | 2020年 / 26卷 / 03期
关键词：
Gearbox; Kernel density estimation; Nonparametric; Remaining useful life prediction; Stochastic filtering;
D O I：
10.13196/j.cims.2020.03.006
中图分类号：
学科分类号：
摘要：
Aiming at the problem that state degradation model structure hypothesis in the process of remaining useful life prediction of the wind turbine gearbox, a real-time remaining useful life prediction method combining kernel density estimation and stochastic filtering theory was proposed. This method used the kernel density estimation method from the data itself to estimate the probability density function of the gearbox continuous degradation state, and obtained the degraded state probability density function based on real-time state monitoring data, and then used the real-time condition monitoring data to update the stochastic filter recurrence model parameters to predict the real-time residual life of the gearbox. Effectiveness of the proposed method was verified by the test of gearbox. © 2020, Editorial Department of CIMS. All right reserved.
引用
收藏
页码：632 / 640
页数：8
相关论文
共 22 条
  • [1] Jin X., Sun Y., Shan J., Et al., Fault diagnosis and prognosis for wind turbines: An over-view, Journal of Instrumentation, 38, 5, pp. 1041-1053, (2017)
  • [2] Lin G., Jia Y., Sun L., Et al., Notice of retraction remaining useful life prediction of gearbox based on a nonlinear state space model, Proceedings of International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering, pp. 1819-1822, (2013)
  • [3] Zhao F., Tian Z., Zeng Y., Uncertainty quantification in gear remaining useful life prediction through an integrated prognostics method, IEEE Transactions on Reliability, 62, 1, pp. 146-159, (2013)
  • [4] Li C., Research on remaining useful lifepredic-tion of mechanical equipment based on monitoring data, (2014)
  • [5] Yang H., Jia X., Ren S., Review of data-driven aeroengine fault diagnosis and prognosis methods, Aerospace Precision Manufacturing Technology, 52, 5, pp. 6-9, (2016)
  • [6] Shi H., Zeng J., Prediction of real-time remaining useful life and maintenance decision model considering imperfect preventive maintenance, Computer Integrated Manufacturing Systems, 20, 9, pp. 2259-2266, (2014)
  • [7] Sun L., Tang X., Zhang X., Et al., Study of gearbox residual life prediction based on stochastic filtering modeling, Mechanical Transmission, 35, 10, pp. 56-60, (2011)
  • [8] Lin G., Jia Y., Sun L., Et al., Research on an gearbox residual life prediction approach based on EKF-SSM, Mechanical Strength, 36, 4, pp. 614-619, (2014)
  • [9] Sun L., Jia Y., Cai L., Et al., Residual useful life prediction of gearbox based on particle filtering parameter estimation method, Journal of Vibration and Shock, 32, 6, pp. 6-12, (2013)
  • [10] Wang Z., Hu C., Wang W., Et al., A case study of rem-aining storage life prediction using stochastic fil-tering with the influence of condition monitoring, Reliability Engineering & System Safety, 132, pp. 186-195, (2014)
123