[1]雷文平,邹冬良,陈世金,等.贝叶斯变点检测的滚动轴承剩余寿命预测方法[J].郑州大学学报(工学版),2025,46(06):93-101.[doi:10.13705/j.issn.1671-6833.2025.03.013]
　LEI Wenping,ZOU Dongliang,CHEN Shijin,et al.A Rolling Bearing Remaining Useful Life Prediction Method Based on Bayesian Change Point Detection[J].Journal of Zhengzhou University (Engineering Science),2025,46(06):93-101.[doi:10.13705/j.issn.1671-6833.2025.03.013]
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贝叶斯变点检测的滚动轴承剩余寿命预测方法()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
46
期数:
2025年06期
页码:
93-101
栏目:
出版日期:
2025-10-22

文章信息/Info

Title:
A Rolling Bearing Remaining Useful Life Prediction Method Based on Bayesian Change Point Detection
文章编号:
1671-6833(2025)06-0093-09
作者:
雷文平1;  邹冬良2;  陈世金2;  黄广众1;  董　星1
1.郑州大学 机械与动力工程学院,河南 郑州 450001;2.五冶集团上海有限公司工程技术服务公司,上海 200400
Author(s):
LEI Wenping1;  ZOU Dongliang2;  CHEN Shijin2;  HUANG Guangzhong1;  DONG Xing1
1.School of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou 450001, China; 2.Engineering Technology Service Company, MCC5 Group, Shanghai 200400, China
关键词:
寿命预测;  滚动轴承;  贝叶斯变点检测;  随机退化设备
Keywords:
remaining useful life prediction;  rolling bearing;  bayesian changepoint detection;  stochastic deteriorating equipment
分类号:
TH133TH17TP273
DOI:
10.13705/j.issn.1671-6833.2025.03.013
文献标志码:
A
摘要:
针对滚动轴承运行退化呈现随机变点的多阶段特征,提出了一种新型的多阶段退化过程剩余寿命预测方法。首先,以离线历史数据估计各阶段模型的先验参数;其次,针对单一在线设备,通过贝叶斯变点检测方法进行变点的实时检测,采用贝叶斯更新方法在变点出现前对第1阶段参数进行更新,变点出现后对第2阶段进行更新;最后,利用多阶段模型进行剩余寿命预测。数值仿真和实例研究结果表明:基于贝叶斯变点检测的滚动轴承寿命预测方法可以提高85%的变点检测精度,进而实现高精度的多阶段剩余寿命预测。
Abstract:
To address the multi-stage characteristics of rolling bearing degradation with random change points, in this paper a novel method was proposed to predict the remaining useful life (RUL) of multi-stage degradation processes. Initially, the prior parameters of each stage model were estimated using offline historical data. Then, for a single online device, real-time change point detection was performed using the Bayesian change point detection method. The Bayesian updating approach was adopted to update the parameters of the first stage before the change point occurs and the second stage after the change point. Subsequently, the multi-stage model was utilized for RUL prediction. Numerical simulations and case studies showed that the rolling bearing life prediction method based on Bayesian change point detection could improve change point detection accuracy by 85%, thereby achieving highprecision multi-stage RUL prediction.

参考文献/References:

[1]陈远航. 滚动轴承剩余寿命预测算法研究及监测软件开发[D]. 哈尔滨: 哈尔滨工业大学, 2020. 

CHEN Y H. Research on prediction algorithm of residual life of rolling bearing and development of monitoring software[D]. Harbin: Harbin Institute of Technology, 2020. 
[2]刘洋, 李凌均, 王宇, 等. 基于FIF-CYCBD的滚动轴承故障特征提取方法研究[J]. 郑州大学学报(工学版), 2022, 43(4): 35-40. 
LIU Y, LI L J, WANG Y, et al. Fault feature extraction method of rolling bearings based on FIF-CYCBD[J]. Journal of Zhengzhou University (Engineering Science), 2022, 43(4): 35-40. 
[3]WEN Y X, WU J G, YUAN Y. Multiple-phase modeling of degradation signal for condition monitoring and remaining useful life prediction[J]. IEEE Transactions on Reliability, 2017, 66(3): 924-938. 
[4]WANG W B, ZHANG W J. Early defect identification: application of statistical process control methods[J]. Journal of Quality in Maintenance Engineering, 2008, 14 (3): 225-236. 
[5]司小胜, 胡昌华. 数据驱动的设备剩余寿命预测理论及应用[M]. 北京: 国防工业出版社, 2016. 
SI X S, HU C H. Data-driven remaining useful life prediction theory and applications for equipment[M]. Beijing: National Defense Industry Press, 2016. 
[6]TSENG S T, TANG J, KU I H. Determination of burn-in parameters and residual life for highly reliable products [J]. Naval Research Logistics (NRL), 2003, 50(1): 1-14. 
[7]PENG C Y, TSENG S T. Mis-specification analysis of linear degradation models[J]. IEEE Transactions on Reliability, 2009, 58(3): 444-455. 
[8]LEE M Y, TANG J. A modified EM-algorithm for estimating the parameters of inverse Gaussian distribution based on time-censored Wiener degradation data[J]. Statistica Sinica, 2007,17(3):873. 
[9]JOSEPH V R, YU I T. Reliability improvement experiments with degradation data[J]. IEEE Transactions on Reliability, 2006, 55(1): 149-157. 
[10] GEBRAEEL N, LAWLEY M, LI R, et al. Residual-life distributions from component degradation signals: a Bayesian approach[J]. IIE Transactions, 2005, 37(6): 543-557. 
[11]史华洁, 薛颂东. 退化数据驱动的设备剩余寿命在线预测[J]. 计算机工程与应用, 2016, 52(23): 249-254. 
SHI H J, XUE S D. Degradation data driven online prediction for equipment residual life[J]. Computer Engineering and Applications, 2016, 52(23): 249-254. 
[12]WANG Y, PENG Y Z, ZI Y Y, et al. A two-stage datadriven-based prognostic approach for bearing degradation problem[J]. IEEE Transactions on Industrial Informatics, 2016, 12(3): 924-932. 
[13]黄子怡. 基于多退化阶段评估的滚动轴承剩余使用寿命预测方法[D]. 长沙: 中南大学, 2023. 
HUANG Z Y. Prediction method of remaining service life of rolling bearing based on multi-degradation stage evaluation[D]. Changsha: Central South University, 2023. 
[14] NG T S. An application of the EM algorithm to degradation modeling[J]. IEEE Transactions on Reliability, 2008, 57(1): 2-13.
[15]WANG P P, TANG Y C, BAE S J, et al. Bayesian analysis of two-phase degradation data based on change-point Wiener process[J]. Reliability Engineering & System Safety, 2018, 170: 244-256. 
[16]董青, 郑建飞, 胡昌华, 等. 基于两阶段自适应Wiener过程的剩余寿命预测方法[J]. 自动化学报, 2022, 48(2): 539-553. 
DONG Q, ZHENG J F, HU C H, et al. Remaining useful life prognostic method based on two-stage adaptive Wiener process[J]. Acta Automatica Sinica, 2022, 48 (2): 539-553. 
[17] ARUNAN A, QIN Y, LI X L, et al. A change point detection integrated remaining useful life estimation model under variable operating conditions[J]. Control Engineering Practice, 2024, 144: 105840. 
[18] ADAMS R P, MACKAY D J C. Bayesian online changepoint detection[EB/OL]. (2007-10-19)(2024-1009). http:/ /arxiv.org/pdf/0710.3742. 
[19]张正新, 胡昌华, 高迎彬, 等. 多阶段随机退化设备剩余寿命预测方法[J]. 系统工程学报, 2017, 32 (1): 1-7. 
ZHANG Z X, HU C H, GAO Y B, et al. A residual useful life prediction approach for equipments with multistate stochastic degradation[J]. Journal of Systems Engineering, 2017, 32(1): 1-7. 
[20] ZHANG J X, HU C H, HE X, et al. A novel lifetime estimation method for two-phase degrading systems[J]. IEEE Transactions on Reliability, 2019, 68 (2): 689-709. 
[21]胡昌华, 樊红东, 王兆强. 设备剩余寿命预测与最优维修决策[M]. 北京: 国防工业出版社, 2018. 
HU C H, FAN H D, WANG Z Q. Residual life prediction and optimal maintenance decision for a piece of equipment[M]. Beijing: National Defense Industry Press, 2018. 
[22]雷亚国, 韩天宇, 王彪, 等. XJTU-SY滚动轴承加速寿命试验数据集解读[J]. 机械工程学报, 2019, 55 (16): 1-6. 
LEI Y G, HAN T Y, WANG B, et al. XJTU-SY rolling element bearing accelerated life test datasets: a tutorial [J]. Journal of Mechanical Engineering, 2019, 55 (16): 1-6.

更新日期/Last Update: 2025-10-21