REMAINING USEFUL LIFE PREDICTION FOR WIND TURBINE SLEWING BEARINGS BASED ON MUTATION POINT DETECTION AND IMPROVED PARTICLE FILTERING

Liu Mingjun; Wang Shuangchuan; Dong Zengshou

doi:10.19912/j.0254-0096.tynxb.2024-1354

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (2) : 408-417. DOI: 10.19912/j.0254-0096.tynxb.2024-1354

REMAINING USEFUL LIFE PREDICTION FOR WIND TURBINE SLEWING BEARINGS BASED ON MUTATION POINT DETECTION AND IMPROVED PARTICLE FILTERING

Liu Mingjun^1,2, Wang Shuangchuan^2,3, Dong Zengshou^2,3

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Abstract

A remaining useful life prediction method based on the Wiener process and an improved particle filter is proposed to address the challenge of accurately estimating the remaining life of wind turbine slewing bearings due to their accelerated degradation under harsh environmental conditions and sudden operational scenarios. Firstly, the mutation point in the degradation process is identified using a mutation point detection method, and the traditional particle filter algorithm is improved. Secondly, the state space model with mutation points is constructed using a nonlinear Wiener process, and the probability density function of the remaining useful life and the parameter update formula are derived. Finally, data from wind turbine slewing bearings are used as an example to verify the effectiveness and superiority of the proposed method.

Key words

wind turbines / remaining useful life prediction / Wiener process / mutation point / particle filter

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Liu Mingjun, Wang Shuangchuan, Dong Zengshou. REMAINING USEFUL LIFE PREDICTION FOR WIND TURBINE SLEWING BEARINGS BASED ON MUTATION POINT DETECTION AND IMPROVED PARTICLE FILTERING[J]. Acta Energiae Solaris Sinica. 2026, 47(2): 408-417 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1354

References

[1] ZHANG B, WANG H, TANG Y, et al.Residual useful life prediction for slewing bearing based on similarity under different working conditions[J]. Experimental techniques, 2018, 42(3): 279-289.
[2] FAN L C, CHAI Y, CHEN X L.Trend attention fully convolutional network for remaining useful life estimation[J]. Reliability engineering & system safety, 2022, 225: 108590.
[3] CHEN X D, SUN X L, SI X S, et al.Remaining useful life prediction based on an adaptive inverse Gaussian degradation process with measurement errors[J]. IEEE access, 2020, 8: 3498-3510.
[4] 刘卫东, 文根, 鄢伟安, 等. 基于退化数据和非线性伽马过程的光伏组件现场寿命评估[J]. 计算机集成制造系统, 2021, 27(12): 3494-3502.
LIU W D, WEN G, YAN W A, et al.Field lifetime assessment of photovoltaic modules based on degradation data-driven and nonlinear Gamma processes[J]. Computer integrated manufacturing systems, 2021, 27(12): 3494-3502.
[5] ZHANG Z X, SI X S, HU C H, et al.Degradation data analysis and remaining useful life estimation: a review on Wiener-process-based methods[J]. European journal of operational research, 2018, 271(3): 775-796.
[6] LI H Q, ZHANG Z X, LI T M, et al.A review on physics-informed data-driven remaining useful life prediction: challenges and opportunities[J]. Mechanical systems and signal processing, 2024, 209: 111120.
[7] 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.
[8] ZHANG X Y, LIU D, LEI B Y, et al.An intelligent particle filter with resampling of multi-population cooperation[J]. Digital signal processing, 2021, 115: 103084.
[9] 丁显, 徐进, 黎曦琳, 等. 融合维纳过程和粒子滤波的风力发电机轴承剩余寿命预测[J]. 太阳能学报, 2022, 43(12): 248-255.
DING X, XU J, LI X L, et al.Remaining life prediction of wind turbine bearing based on Wiener process and particle filter[J]. Acta energiae solaris sinica, 2022, 43(12): 248-255.
[10] 陈伟, 雷欢, 裴婷婷, 等. 基于退化轨迹和Wiener模型的光伏组件剩余寿命预测方法[J]. 太阳能学报, 2023, 44(7): 175-181.
CHEN W, LEI H, PEI T T, et al.Remaining life prediction method of photovoltaic modules based on degradation trajectory and Wiener model[J]. Acta energiae solaris sinica, 2023, 44(7): 175-181.
[11] 李乃鹏, 蔡潇, 雷亚国, 等. 一种融合多传感器数据的数模联动机械剩余寿命预测方法[J]. 机械工程学报, 2021, 57(20): 29-37, 46.
LI N P, CAI X, LEI Y G, et al.A model-data-fusion remaining useful life prediction method with multi-sensor fusion for machinery[J]. Journal of mechanical engineering, 2021, 57(20): 29-37, 46.
[12] AN D, CHOI J H, KIM N H.Prognostics 101: a tutorial for particle filter-based prognostics algorithm using Matlab[J]. Reliability engineering & system safety, 2013, 115: 161-169.
[13] 田德艳, 张小川, 邹司宸, 等. L2正则化粒子滤波在水下无人平台纯方位角跟踪的应用[J]. 舰船科学技术, 2020, 42(23): 111-116.
TIAN D Y, ZHANG X C, ZOU S C, et al.Application of L2 regularized particle filter in pure azimuth tracking of underwater unmanned platform[J]. Ship science and technology, 2020, 42(23): 111-116.
[14] 唐中信, 叶今禄, 易健彬, 等. 基于扩展卡尔曼滤波的动力电池 SOC估计方法[J]. 内燃机与动力装置, 2024, 41(1): 94-98, 104.
TANG Z X, YE J L, YI J B, et al.SOC estimation of power battery based on extended Kalman filter[J]. Internal combustion engine & powerplant, 2024, 41(1): 94-98, 104.
[15] 姚露, 聂晓根, 黄汉阳, 等. 无迹卡尔曼滤波算法对UWB/IMU组合定位的研究[J]. 机械科学与技术, 2025, 44(6): 1033-1040.
YAO L, NIE X G, HUANG H Y, et al.Research on UWB/IMU combined location based on unscented Kalman filter algorithm[J]. Mechanical science and technology for aerospace engineering, 2025, 44(6): 1033-1040.
[16] YIN S, ZHU X P.Intelligent particle filter and its application to fault detection of nonlinear system[J]. IEEE transactions on industrial electronics, 2015, 62(6): 3852-3861.
[17] 毛鑫萍, 李贝, 张才强. 粒子滤波和正则粒子滤波算法性能研究[J]. 电声技术, 2014, 38(6): 67-71.
MAO X P, LI B, ZHANG C Q.Comparative performance study of particle filter and regularized particle filter[J]. Audio engineering, 2014, 38(6): 67-71.
[18] YE L H, CHEN S J, SHI Y F, et al.Remaining useful life prediction of lithium-ion battery based on chaotic particle swarm optimization and particle filter[J]. International journal of electrochemical science, 2023, 18(5): 100122.
[19] 封杨, 黄筱调, 洪荣晶, 等. 基于数据驱动的回转支承性能退化评估方法[J]. 中南大学学报(自然科学版), 2017, 48(3): 684-693.
FENG Y, HUANG X D, HONG R J, et al.A multi-dimensional data-driven method for large-size slewing bearings performance degradation assessment[J]. Journal of central south university (science and technology), 2017, 48(3): 684-693.
[20] LIU X T, YANG J C, WANG L, et al.Bayesian information criterion based data-driven state of charge estimation for lithium-ion battery[J]. Journal of energy storage, 2022, 55: 105669.
[21] 李天梅, 司小胜, 刘翔, 等. 大数据下数模联动的随机退化设备剩余寿命预测技术[J]. 自动化学报, 2022, 48(9): 2119-2141.
LI T M, SI X S, LIU X, et al.Data-model interactive remaining useful life prediction technologies for stochastic degrading devices with big data[J]. Acta automatica sinica, 2022, 48(9): 2119-2141.
[22] PANG Z N, SI X S, HU C H, et al.A Bayesian inference for remaining useful life estimation by fusing accelerated degradation data and condition monitoring data[J]. Reliability engineering & system safety, 2021, 208: 107341.