报告详情
Remining Useful Life Prediction Using Graph Neural Networks for Rolling Bearing
编号：145
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更新：2024-10-23 10:02:35 浏览：149次
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报告开始：暂无开始时间（Asia/Shanghai）
报告时间：暂无持续时间
所在会场：[暂无会议] [暂无会议段]
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摘要
Remaining Useful Life (RUL) prediction is a necessary tool for condition monitoring and health management of rotating machinery, which is very important to ensure safe and economical operation of rotating machinery. Since traditional prediction methods are slightly insufficient in extracting local spatio-temporal feature information, this study introduces a method of predicting the remaining life of bearings by using the Graph Convolutional Network (GCN). Firstly, the amplitude of signal samples is used as features to construct nodes. Secondly, edge features are generated based on the temporal correlation between the front and back nodes to capture the local temporal feature information in the sample signals. Based on the constructed nodes and edges, the PathGraph is generated. Meanwhile, a graph neural network prediction framework is built to mine and learn the temporal feature information in the graph structure to achieve end-to-end bearing lifetime prediction. Experimental results of this study verify the effectiveness of the proposed method in predicting the remaining useful life of bearings.
关键词
Deep learning, Remaining useful life prediction, graph convolu-tional network, PathGraph, spatial dependency
报告人
JinHuaiwang
master student Anhui University稿件作者
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重要日期
-
会议日期
10月31日
2024
至11月03日
2024
-
09月30日 2024
初稿截稿日期
-
11月12日 2024
注册截止日期
主办单位
Anhui University
Xi’an Jiaotong University
Harbin Institute of Technology
IEEE Instrumentation & Measurement Society
Xi’an Jiaotong University
Harbin Institute of Technology
IEEE Instrumentation & Measurement Society
联系方式
- Mrs. icsmd
- ic******@163.com
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