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

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[14] Pronostia： an experimental platform for bearings accelerated degradation tests ［C］, IEEE International Conference on Prognostics and Health Management, pp. 1-8, (2012)

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