Bearing Remaining Life Prediction Based on Full Convolutional Layer Neural Networks

被引：8

作者：

Zhang J. ^{[1
]}

Zou Y. ^{[1
]}

Deng J. ^{[1
]}

Zhang X. ^{[1
]}

机构：

[1] School of Mechanical Engineering, Southwest Jiaotong University, Chengdu

来源：

Zhongguo Jixie Gongcheng/China Mechanical Engineering | 2019年 / 30卷 / 18期

关键词：

Bearing; Full convolutional layer; Neural network; Remaining life prediction;

D O I：

10.3969/j.issn.1004-132X.2019.018.014

中图分类号：

学科分类号：

摘要：

Traditional method of data-driven bearing remaining life prediction was based on knowledge and experience, and the degradation index was established manually which was time-consuming and labor-intensive. Therefore, the convolutional neural network (CNN) was used to perform feature self-learning and remaining life prediction. All connected layers in the traditional CNN were replaced with convolutional layers and pooling layers to reduce the training parameters of the neural network. Weighted average method was used to denoise the prediction results. Dataset of the accelerated life test of the bearings shows the effectiveness of the proposed method. © 2019, China Mechanical Engineering Magazine Office. All right reserved.

引用

页码：2231 / 2235

页数：4

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