Remining Useful Life Prediction Using Graph Neural Networks for Rolling Bearing
ID：145
Submission ID：79 View Protection：ATTENDEE
Updated Time：2024-10-23 10:02:35 Hits：41
Poster Presentation
Start Time：Pending （Asia/Shanghai）
Duration：Pending
Session：[No Session] » [No Session Block]
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Abstract
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.
Keywords
Deep learning, Remaining useful life prediction, graph convolu-tional network, PathGraph, spatial dependency
Speaker
JinHuaiwang
master student Anhui UniversitySubmission Author
All comments
15th August 2024 31st August 2024- Manuscript Submission
15th September 2024 - Acceptance Notification
1st October 2024 - Camera Ready Submission
1st October 2024 – Early Bird Registration
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