REMAINING LIFE PREDICTION OF WIND TURBINE BEARING BASED ON WIENER PROCESS AND PARTICLE FILTER

Ding Xian; Xu Jin; Li Xilin; Teng Wei; Gong Yongli

doi:10.19912/j.0254-0096.tynxb.2021-0737

PDF(3807 KB)

Acta Energiae Solaris Sinica ›› 2022, Vol. 43 ›› Issue (12) : 248-255. DOI: 10.19912/j.0254-0096.tynxb.2021-0737

REMAINING LIFE PREDICTION OF WIND TURBINE BEARING BASED ON WIENER PROCESS AND PARTICLE FILTER

Ding Xian^1,2, Xu Jin^1,2, Li Xilin³, Teng Wei³, Gong Yongli^1,2

Author information +

History +

Abstract

This paper proposes a method for predicting the remaining life of rolling bearings based on the combination of the Wiener process and the particle filter. The Wiener process is introduced into the particle filter state space model to make full use of its random incremental nature to enhance the nonlinear expression ability of the model and improve the accuracy of prediction. Weak-tracking particle filter strategy is used to adjust the Wiener process to solve the problem of too large of probability density distribution. The method in this paper has been verified by the test data of the bearing of the test bench and the wind turbine bearing on-site, accurately predicting the remaining life of the bearing.

Key words

wind turbines / Wiener process / particle filter / remaining life / bearing performance degradation

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

Ding Xian, Xu Jin, Li Xilin, Teng Wei, Gong Yongli. REMAINING LIFE PREDICTION OF WIND TURBINE BEARING BASED ON WIENER PROCESS AND PARTICLE FILTER[J]. Acta Energiae Solaris Sinica. 2022, 43(12): 248-255 https://doi.org/10.19912/j.0254-0096.tynxb.2021-0737

References

[1] 李辉, 胡姚刚, 唐显虎, 等. 并网风电机组在线运行状态评估方法[J]. 中国电机工程学报, 2010, 30(33): 103-109.
LI H, HU Y G, TANG X H, et al.Method for on-line operating conditions assessment for a grid-connected wind turbine generator system[J]. Proceedings of the CSEE, 2010, 30(33): 103-109.
[2] 陈雪峰, 郭艳婕, 许才彬, 等. 风电装备故障诊断与健康监测研究综述[J]. 中国机械工程, 2020, 31(2): 175-189.
CHEN X F, GUO Y J, XYU C B, et al.Review of fault diagnosis and health monitoring for wind power equipment[J]. China mechanical engineering, 2020, 31(2): 175-189.
[3] LEE J, WU F, ZHAO W, et al.Prognostics and health management design for rotary machinery systems-reviews, methodology and applications[J]. Mechanical systems and signal processing, 2014, 42(1-2): 314-334.
[4] 刘大同, 周建宝, 郭力萌, 等. 锂离子电池健康评估和寿命预测综述[J]. 仪器仪表学报, 2015, 36(1): 1-16.
LIU D T, ZHOU J B, GUO L M, et al.Survey on lithium-ion battery health assessment and cycle life estimation[J]. Chinese journal of scientific instrument, 2015, 36(1): 1-16.
[5] 彭宇, 刘大同. 数据驱动故障预测和健康管理综述[J]. 仪器仪表学报, 2014, 35(3): 481-495.
PENG Y, LIU D T.Data-driven prognostics and health management: a review of recent advances[J]. Chinese journal of scientific instrument, 2014, 35(3): 481-495.
[6] MAIO F D, TSUI K L, ZIO E.Combining relevance vector machines and exponential regression for bearing residual life estimation[J]. Mechanical systems and signal processing, 2012, 31: 405-427.
[7] SHAO Y, NEZU K.Prognosis of remaining bearing life using neural networks[J]. Proceedings of the institution of mechanical engineers, part 1: journal of systems and control engineering, 2000, 214(3): 217-230.
[8] 范彬, 胡雷, 胡茑庆. 退化速率跟踪粒子滤波在剩余使用寿命预测中的应用[J]. 国防科技大学学报, 2015, 37(3): 161-166.
FAN B, HU L, HU N Q.Remaining useful life prediction based on degradation rate tracking particle filter[J]. Journal of National University of Defense Technology, 2015, 37(3): 161-166.
[9] 刘淑杰, 郝昆昆, 王永, 等. 基于改进粒子滤波算法的动力锂离子电池荷电状态估计[J]. 大连理工大学学报, 2020, 60(4): 392-401.
LIU S J, HAO K K, WANG Y, et al.State of charge estimation of power lithium-ion battery based on improved particle filter algorithms[J]. Journal of Dalian University of Technology, 2020, 60(4): 392-401.
[10] TENG W, HAN C, HU Y K, et al.A robust model-based approach for bearing remaining useful life prognosis in wind turbines[J]. IEEE access, 2020, 8: 47133-47143.
[11] 朱晓栋, 陈则王. 基于维纳过程的电池剩余使用寿命预测[J]. 机械制造与自动化, 2018, 47(4): 197-200.
ZHU X D, CHEN Z W.Remaining useful life prediction of battery based on wiener process[J]. Machine building and automation, 2018, 47(4): 197-200.
[12] 胡姚刚, 李辉, 廖兴林, 等. 风电轴承性能退化建模及其实时剩余寿命预测[J]. 中国电机工程学报, 2016, 36(6): 1643-1649.
HU Y G, LI H, LIAO X L, et al.Performance degradation model and prediction method of real-time remaining life for wind turbine bearings[J]. Proceedings of the CSEE, 2016, 36(6): 1643-1649.
[13] 黄亮, 刘君强, 贡英杰. 基于Wiener过程的发动机多阶段剩余寿命预测[J]. 北京航空航天大学学报, 2018, 44(5): 1081-1087.
HUANG L, LIU J Q, GONG Y J.Multi-phase residual life prediction of engines based on Wiener process[J]. Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(5): 1081-1087.
[14] 黄泽毅. 基于自适应维纳过程的剩余寿命预测方法研究[D]. 杭州: 浙江大学, 2015.
HUANG Z Y.Research on remaining useful life prediction based on the adaptive Wiener process[D]. Hangzhou: Zhejiang University, 2015.
[15] WANG W B, CARR M, XU W J, et al.A model for residual life prediction based on Brownian motion with an adaptive drift[J]. Microelectronics reliability, 2011, 51(2): 285-293.
[16] ARULAMPALAM M S, MASKELL S, GORDON N, et al.A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking[J]. IEEE transactions on signal processing, 2002, 50(2): 174-188.
[17] HU Y, BARALDI P, MAIO F D, et al.A particle filtering and kernel smoothing-based approach for new design component prognostics[J]. Reliability engineering and system safety, 2015, 134: 19-31.
[18] 金晓航, 李建华, 孙毅. 基于二元维纳过程的轴承剩余寿命预测[J]. 仪器仪表学报, 2018, 39(6): 89-95.
JIN X H, LI J H, SUN Y.Bearing remaining useful life prediction based on two-dimensional Wiener process[J]. Chinese journal of scientific instrument, 2018, 39(6): 89-95.
[19] NECTOUX P, GOURIVEAU R, MEDJAHER K, et al.PRONOSTIA: an experimental platform for bearings accelerated degradation tests[C]//IEEE International Conference on Prognostics and Health Management, PHM’12., Denver, Colorado, USA, 2012.
[20] WANG B, LEI Y G, LI N B, et al.A hybrid prognostics approach for estimating remaining useful life of rolling element bearings[J]. IEEE transactions on reliability, 2020, 69(1): 401-412.
[21] ISO 10816-21:2015, Measurement and evaluation of the mechanical vibration of wind energy turbines and their components onshore wind energy turbines with gears[S].
[22] WANG D, YANG F F, ZHAO Y, et al.Prognostics of lithium-ion batteries based on state space modeling with heterogeneous noise variances[J]. Microelectronics reliability, 2017, 75: 1-8.
[23] AHMAD W, KHAN S A, ISLAM M M M, et al. A reliable technique for remaining useful life estimation of rolling element bearings using dynamic regression models[J]. Reliability engineering & system safety, 2019, 184: 67-76.