Research on Remaining Useful Life Prediction of Rolling Bearings Based on Fusion Feature and Model-Data-Fusion

被引：0

作者：

Wang Q. ^{[1
]}

Huang Q. ^{[2
]}

Jiang X. ^{[2
]}

Xu K. ^{[3
]}

Zhu Z. ^{[2
]}

机构：

[1] School of Optical and Electronic Information, Suzhou City University, Suzhou

[2] School of Rail Transportation, Soochow University, Suzhou

[3] College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing

来源：

Zhendong Ceshi Yu Zhenduan/Journal of Vibration, Measurement and Diagnosis | 2023年 / 43卷 / 04期

关键词：

fusion feature; model-data-fusion; remaining useful life prediction; rolling bearings; smooth filtering;

D O I：

10.16450/j.cnki.issn.1004-6801.2023.04.011

中图分类号：

学科分类号：

摘要：

Due to the interference of random noise and the degradation characteristics of rolling bearings，traditional model-data-fusion based remaining useful life（RUL）prediction method of rolling bearings might be affected. Thus，a novel RUL prediction method is proposed based on the fusion indictor and model-data-fusion to improve the accuracy of RUL prediction of rolling bearings. First，the principle component analysis and exponentially weighted moving average algorithm are used to fuse the multiple charactering features for constructing a monotonous fusion indictor. Then，a determining scheme for the first predicting time is built on 3σ criteria to trigger the RUL prediction process，which can avoid the invalidity of the prediction process. Lastly，the Rauch-Tung-Striebel smooth filter algorithm is embedded into the prediction model to reduce the random fluctuation and achieve the reliable RUL of rolling bearing. Simulated and experimental cases demonstrate the effectiveness of the proposed method and its superiority over the traditional model-data-fusion RUL prediction method. © 2023 Nanjing University of Aeronautics an Astronautics. All rights reserved.

引用

页码：705 / 711+828

共 14 条

[1] HE Zhengjia, CAO Hongrui, ZI Yangyang, Et al., Developments and thoughts on operational reliability assessment of mechanical equipment［J］, Journal of Mechanical Engineering, 50, 2, pp. 171-186, (2014)
[2] ZHANG Xuhui, ZHANG Chao, FAN Hongwei, Et al., Improved fault diagnosis of rolling bearing by fast kurtogram and order analysis［J］, Journal of Vibration，Measurement & Diagnosis, 41, 6, pp. 1090-1095, (2021)
[3] ZHAO W，, Et al., Prognostics and health management design for rotary machinery systems：reviews，methodology and applications ［J］, Mechanical Systems and Signal Processing, 42, 1, pp. 314-334, (2014)
[4] ESPERONMIGUEZ M., A review of physics-based models in prognostics：application to gears and bearings of rotating machinery ［J］, Advances in Mechanical Engineering, 8, 8, pp. 1-21, (2016)
[5] LI N，, GUO L，, Et al., Machinery health prognostics：a systematic review from data acquisition to RUL prediction［J］, Mechanical Systems and Signal Processing, 104, pp. 799-834, (2018)
[6] ZHANG X，, Et al., Locally linear embedding on grassmann manifold for performance degradation assessment of bearings［J］, IEEE Transactions on Reliability, 66, 2, pp. 467-477, (2017)
[7] WANG Yujing, LI Shaopeng, KANG Shouqiang, Et al., Method of predicting remaining useful life of rolling bearing combining CNN and LSTM［J］, Journal of Vibration，Measurement & Diagnosis, 41, 3, pp. 439-446, (2021)
[8] LI R，, Et al., Residual-life distributions from component degradation signals：a bayesian approach ［J］, IIE Transactions, 37, 6, pp. 543-557, (2005)
[9] CHEN M，, 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, 226, 1, pp. 53-66, (2013)
[10] YAN B， MA X，, HUANG G，, Et al., Two-stage physics-based Wiener process models for online RUL prediction in field vibration data ［J］, Mechanical Systems and Signal Processing, 152, (2021)

← 1 2 →