科技创新与应用2025，Vol.15Issue(17)：64-67,71,5.DOI:10.19981/j.CN23-1581/G3.2025.17.013
基于卷积神经网络的轴承剩余使用寿命预测模型研究
付国凯1
作者信息
- 1. 青岛酒店管理职业技术学院,山东 青岛 266100
- 折叠
摘要
Abstract
Bearing plays a key role in mechanical equipment,and the accurate prediction of its remaining service life(RUL)is of great significance for ensuring the reliability and safety of equipment and reducing maintenance costs.In this paper,the prediction model of bearing remaining service life based on convolutional neural network(CNN)is deeply studied.Firstly,the research background and significance of bearing fault prediction are introduced,and the method of collecting and preprocessing bearing fault data is expounded in detail.Then the structure and principle of convolutional neural network are analyzed deeply,and it is applied to the construction of residual service life prediction model of bearing.The model is trained and validated through experiments,with its performance metrics systematically evaluated and compared against traditional prediction methods.Results demonstrate that the CNN-based prediction model achieves superior accuracy and effectiveness in bearing RUL estimation,providing robust support for intelligent maintenance of mechanical equipment.关键词
卷积神经网络/轴承/剩余使用寿命/预测模型/数据采集Key words
convolutional neural network/bearing/remaining useful life/prediction model/data acquisition分类
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付国凯..基于卷积神经网络的轴承剩余使用寿命预测模型研究[J].科技创新与应用,2025,15(17):64-67,71,5.
