Remaining useful life prediction method of rolling bearing based on SKF-KF-Bayes

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Journal of Vibration and Shock ›› 2021, Vol. 40 ›› Issue (19) : 26-31.

XU Yanlei1, QIU Ming1,2, LI Junxing1,2, LIU Lu1, NIU Kaicen1

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Abstract

Accurate prediction for residual life of rolling bearing plays an important role in safe and reliable operation of mechanical equipment. Here, aiming at problems in life prediction of rolling bearing, such as, not being able to accurately distinguish degradation stage of rolling bearing and how to effectively use historical degradation data and real-time monitoring data, a method for rolling bearing performance degradation modeling and remaining useful life (RUL) prediction based on combination of switching Kalman filters(SKF) recognition, Kalman filters (KF) single step prediction and Bayes update was proposed. Firstly, combined with performance monitoring data of rolling bearing vibration signals, SKF method was used to identify the change point of bearing performance degradation. Secondly, the random effect exponential degradation model was used to describe the process of bearing performance degradation, and the maximum likelihood estimation of the model’s unknown parameters was given based on the performance data of the same kind bearings. Then, KF single-step prediction was used to modify the monitoring data at the present moment, and random parameters in the model were updated in real time based on Bayes method to derive the bearing residual life distribution model, and calculate the residual life of rolling bearing. Finally, the applicability and effectiveness of the proposed method were verified through analyzing test data of rolling bearing.

Key words

rolling bearing / remaining life useful (RUL) prediction / switching Kalman filters (SKF) recognition / Kalman filters (KF) single step prediction / Bayes update

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XU Yanlei, QIU Ming, LI Junxing, LIU Lu, NIU Kaicen. Remaining useful life prediction method of rolling bearing based on SKF-KF-Bayes[J]. Journal of Vibration and Shock, 2021, 40(19): 26-31

References

［1］张小丽,陈雪峰,李兵,等.机械重大装备寿命预测综述［J］.机械工程学报,2011,47(11):100-116. 
ZHANG Xiaoli, CHEN Xuefeng, LI Bing, et al.Review of life prediction for mechanical major equipments［J］. Journal of Mechanical Engineering,2011,47(11): 100-116.
［2］ZAIDI S S H, AVIYENTE S, SALMAN M, et al. Prognosis of gear failures in DC starter motors using hidden markov models［J］. IEEE Transactions on Industrial Electronics, 2011,58(5): 1695-1706.
［3］VICHARE N M, PECHT M G. Prognostics and health management of electronics［J］.IEEE Transactions on Components and Packaging Technologies, 2006, 29(1):222-229. 
［4］SI X S, WANG W, HU C H, et al. Remaining useful life estimation: a review on the statistical data driven approaches［J］. European Journal of Operational Research, 2011, 213(1):1-14.
［5］张继冬, 邹益胜, 邓佳林,等. 基于全卷积层神经网络的轴承剩余寿命预测［J］. 中国机械工程, 2019, 30(18):2231-2235.
ZHANG Jidong,ZOU Yisheng,DENG Jialin,et al.Bearing remaining life prediction based on full convolutional layer neural networks［J］.China Mechanical Engineering, 2019, 30(18):2231-2235.
［6］ELFORJANI M. Estimation of remaining useful life of slow speed bearings using acoustic emission signals［J］. Journal of Nondestructive Evaluation, 2016, 35(4):32-62.
［7］于震梁,孙志礼,曹汝男，等.基于支持向量机和卡尔曼滤波的机械零件剩余寿命预测模型研究［J］.兵工学报,2018,39(5):991-997.
YU Zhenliang, SUN Zhili, CAO Runan,et al.Research on remaining useful life predictive model of machine parts based on SVM and Kalman filter［J］.Acta Armamentarii, 2018, 39(5):991-997.
［8］张龙龙. 基于多健康状态评估的滚动轴承剩余寿命预测方法研究［D］. 成都:电子科技大学, 2014.
［9］孙磊, 贾云献, 蔡丽影, 等. 粒子滤波参数估计方法在齿轮箱剩余寿命预测中的应用研究［J］. 振动与冲击, 2013, 32(6):6-12.
SUN Lei,JIA Yunxian, CAI Liying,et al.Residual useful life prediction of gearbox based on particle filtering parameter estimation method［J］.Journal of Vibration and Shock, 2013, 32(6):6-12. 
［10］文娟, 高宏力. 一种基于UPF的轴承剩余寿命预测方法［J］. 振动与冲击, 2018, 37(24):225-230.
WEN Juan,GAO Hongli.Remaining useful life prediction of bearings with the unscented particle filter approach［J］. Journal of Vibration and Shock, 2018, 37(24):225-230.
［11］PENG W,LI Y F,YANG Y J,et al. Leveraging degradation testing and condition monitoring for field reliability analysis with time-varying operating missions［J］. IEEE Transactions on Reliability,2015, 64(4):1367-1382.
［12］PAN D,LIU J B,CAO J.Remaining useful life estimation using an inverse Gaussian degradation model［J］. Neurocomputing,2016,185(12):64-72.
［13］LU C J,MEEKER W O.Using degradation measures to estimate a time-to-failure distribution［J］. Technometrics, 1993,35(2): 161-174.
［14］GEBRAEEL N Z,LAWLEY M A,RONG L I,et al. Residual-life distributions from component degradation signals:a Bayesian approach［J］.IIE Transactions, 2005,37(6): 543-557.
［15］WANG D, YANG F F, TSUI K L, et al. Remaining useful life prediction of lithium-ion batteries based on spherical cubature particle filter［J］. IEEE Transactions on Instrumentation and Measurement, 2016, 65(6):1282-1291.
［16］WANG Y H, DENG C, WU J, et al. Failure time prediction for mechanical device based on the degradation sequence［J］. Journal of Intelligent Manufacturing, 2015, 26(6):1181-1199.
［17］LIM C K R, MBA D. Switching Kalman filter for failure prognostic［J］. Mechanical Systems & Signal Processing, 2015, 52(53):426-435.
［18］BAR S Y, KIRUBARAJAN T, LIX R. Estimation with applications to tracking and navigation［M］.New York: Wiley, 2001.
［19］WANG B, LEI Y G, LI N P, 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.
［20］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 Yaguo,HAN Tianyu,WANG Biao, et al. XJTU-SY rolling element bearing accelerated life test datasets：a tutorial［J］.Journal of Mechanical Engineering, 2019,55 (16):1-6.