A Remaining Useful Life Prediction Approach with Nonuniform Monitoring Conditions for Rolling Bearings

doi:10.3901/JME.2023.23.096

Abstract

Abstract: The modeling of degradation processes relies on the discretization of time and amplitude, and most of the existing degradation modeling methods are based on the assumption of uniform dispersion of time. However, in the daily operation and maintenance of the equipment, due to factors such as sensor failures or operator errors, the available monitoring data can be nonuniform (such as condition monitoring data of rolling bearings as key components of rotating machinery), which results in additional deviations in the degradation model when updating parameters and predicting the remaining life (RUL). Aiming at this problem, a RUL prediction approach with nonuniform monitoring conditions for rolling bearings is proposed. Firstly, the Brownian motion term associated with monitoring intervals is constructed to accurately characterize the temporal variability of random parameters of the degradation model with nonuniform intervals. Then, based on the expectation maximization (EM) algorithm and the square-root cubature Kalman filter (SCKF), a dynamic parameter inference method of the degradation model with nonuniform intervals is proposed, and the degradation state and RUL adaptive estimation of rolling bearings are accomplished. The effectiveness of the proposed approach for predicting the RUL is verified by means of rolling bearing full life test examples. The results show that under nonuniform monitoring conditions, the proposed approach obtains higher prediction accuracy and better fitting performance compared with other exponential degradation models.

Key words: rolling bearing, remaining useful life prediction, nonuniform monitoring interval, square-root cubature Kalman filter

CLC Number:

TH17

WANG Yu, LIU Qiufa, PENG Yizhen. A Remaining Useful Life Prediction Approach with Nonuniform Monitoring Conditions for Rolling Bearings[J]. Journal of Mechanical Engineering, 2023, 59(23): 96-104.

References

[1] JIN Xiaohang，SUN Yi，QUE Zijun，et al. Anomaly detection and fault prognosis for bearings[J]. IEEE Transactions on Instrumentation and Measurement，2016，65(9):2046-2054.
[2] LEI Yaguo，LI Naipeng，GUO Liang，et al. Machinery health prognostics:A systematic review from data acquisition to RUL prediction[J]. Mechanical Systems and Signal Processing，2018，104:799-834.
[3] SI Xiaosheng，WANG Wenbin，CHEN Maoyin，et al. A degradation path-dependent approach for remaining useful life estimation with an exact and closed-form solution[J]. European Journal of Operational Research，2013，226:53-66.
[4] LI Naipeng，LEI Yaguo，LIN Jing，et al. An improved exponential model for predicting remaining useful life of rolling element bearings[J]. IEEE Transactions on Industrial Electronics，2015，62(12):7762-7773.
[5] WANG Yu，PENG Yizhen，ZI Yanyang，et al. A two-stage data-driven-based prognostic approach for bearing degradation problem[J]. IEEE Transactions on Industrial Informatics，2016，12(3):924-932.
[6] LEI Yaguo，LI Naipeng，GONTARZ S，et al. A model-based method for remaining useful life prediction of machinery[J]. IEEE Transactions on Reliability，2016，65(3):1314-1326.
[7] SU Xiaohong，WANG Shuai，PECHT M，et al. Interacting multiple model particle filter for prognostics of lithium-ion batteries[J]. Microelectronics Reliability，2017，70:59-69.
[8] CUI Lingli，WANG Xin，XU Yonggang，et al. A novel switching unscented Kalman filter method for remaining useful life prediction of rolling bearing[J]. Measurement，2019，135:678-684.
[9] ZHAI Qingqing，YE Zhisheng. RUL prediction of deteriorating products using an adaptive Wiener process model[J]. IEEE Transactions on Industrial Informatics，2017，13(6):2911-2921.
[10] SARKKA S. Bayesian filtering and smoothing[M]. New York:Cambridge University Press，2013.
[11] ARASARATNAM I，HAYKIN S. Cubature Kalman filters[J]. IEEE Transactions on Automatic Control，2009，54(6):1254-1269.
[12] SI Xiaosheng. An adaptive prognostic approach via nonlinear degradation modeling:Application to battery data[J]. IEEE Transactions on Industrial Electronics，2015，62(8):5082-5096.
[13] HUANG Zeyi，XU Zhengguo，WANG Wenhai，et al. Remaining useful life prediction for a nonlinear heterogeneous Wiener process model with an adaptive drift[J]. IEEE Transactions on Reliability，2015，64(2):687-700.
[14] KOKKALA J，SOLIN A，SARKKA S. Expectation maximization based parameter estimation by sigma-point and particle smoothing[C]//Proceedings of the IEEE Conference on Information Fusion. Salamanca:IEEE Conference on Information. Fusion，2014:1-8.
[15] WANG Biao，LEI Yaguoi，LI Naipeng，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.
[16] 申中杰，陈雪峰，何正嘉，等. 基于相对特征和多变量支持向量机的滚动轴承剩余寿命预测[J]. 机械工程学报，2013，49(2):183-189. SHEN Zhongjie，CHEN Xuefeng，HE Zhengjia，et al. Remaining life predictions of rolling bearing based on relative features and multivariable support vector machine[J]. Journal of Mechanical Engineering，2013，49(2):183-189.
[17] SAXENA A，CELAYA J，SAHA B，et al. Metrics for offline evaluation of prognostic performance[J]. International Journal of Prognostic and Health Manage，2010，1(1):1-20.
[18] LI Xiang，ZHANG Wei，DING Qian. Deep learning-based remaining useful life estimation of bearings using multi-scale feature extraction[J]. Reliability Engineering and System Safety，2019，182:208-218.
[19] TANG Shengjin，GUO Xiaosong，YU Chuanqing，et al. Real time remaining useful life prediction based on nonlinear Wiener based degradation processes with measurement errors[J]. Journal of Central South University，2014，21:4509-4517.
[20] NECTOUX P，GOURIVEAU R，MEDJAHER K，et al. PRONOSTIA:An experimental platform for bearings accelerated degradation tests[C]//Proceedings of the IEEE Conference on Prognostics and Health Management. Denver:IEEE International Conference on Prognostics and Health Management，2012:1-8.