Prediction Method of Remaining Useful Life of Rolling Bearings Based on Improved GcForest

被引：0
|
作者：
Wang Y. [1 ]
Wang S. [1 ]
Kang S. [1 ]
Wang Q. [1 ]
Mikulovich V.I. [2 ]
机构：
[1] School of Electrical and Electronic Engineering, Harbin University of Science and Technology, Harbin, 150080, Heilongjiang Province
[2] Belarusian State University, Minsk
来源：
Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering | 2020年 / 40卷 / 15期
基金：
中国国家自然科学基金;
关键词：
Convolutional neural networks; Deep iterative features; Multi-grained cascade forest; Remaining useful life prediction; Rolling bearing;
D O I：
10.13334/j.0258-8013.pcsee.191730
中图分类号：
学科分类号：
摘要：
For the problems that the existing artificial intelligence methods have poor precision and low computational efficiency in the prediction of remaining useful life (RUL) of rolling bearings, a new method of predicting RUL of rolling bearings was proposed based on deep iterative feature (DIF) cascaded CatBoost (CasCatBoost). This method is an improved new multi-grained cascade forest (gcForest) algorithm. Firstly, the frequency domain signal of rolling bearing was obtained using fast Fourier transform, and the iterative feature (IF) obtained by iterative operation. To reduce the memory consumption, the multi-grained scanning structure in the gcForest was replaced by convolutional neural networks (CNN), the deep feature DIF of IF was extracted, and the performance degradation feature set was constructed. Then, a single CatBoost model that can realize GPU parallel acceleration was integrated, and the determination coefficient R2 was introduced to construct the CasCatBoost structure for improving the representation ability of the model. The average life percentage p of the last cascade layer of the model was selected as the output. Finally, linear function was used to fit p and the RUL of rolling bearing was predicted. PHM2012 database was used for predicting the RUL of rolling bearing, and the prediction average error of the proposed method is 10.57%, the average score is 0.426. © 2020 Chin. Soc. for Elec. Eng.
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页码：5032 / 5042
页数：10
相关论文
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