报告详情
GDGC-AE: A New Approach to Mechanical Anomaly Detection Based on Graph Convolutional Networks and Autoencoders
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更新：2024-10-23 11:34:44
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口头报告
报告开始：2024年11月02日 11:30（Asia/Shanghai）
报告时间：20min
所在会场：[P5] Parallel Session 5 [P5-2] Parallel Session 5（November 2 AM）
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摘要
For the mechanical anomaly detection task, the neural network model trained only on normal data has limitations in multi-working condition anomaly detection and multi-channel information correlation mining. In this paper, a global dynamic graph convolutional autoencoder (GDGC-AE) model based on Chebyshev convolution is proposed to cope with the above problems. Firstly, in terms of graph structure data generation, inter-channel graph data is generated based on graph transformer to extract global channel features. Secondly, dynamic graph convolutional autoencoder is proposed, which dynamically adjusts the feature weights of different edges in the graph structure data through the graph attention mechanism, so as to fully integrate the multi-view feature information and accurately reconstruct the representations reflecting the essential features of normal data when the working conditions change. The combination of the two effectively improves the anomaly detection ability and generalisation performance of the model under complex conditions. Finally, the validation results based on the bearing dataset of Politecnico di Torino, Italy, and the broken teeth dataset of TBD234V12 diesel engine in the laboratory show that the proposed model has an accuracy of 96.13% and 93.75% on the two datasets, respectively, with an excellent generalisation, which provides a more robust and accurate solution for the detection of industrial machinery faults.
关键词
Keywords—anomaly detection, graph convolutional network (GCN), global dynamic graph convolutional autoencoder (GDGC-AE), feature fusion
报告人
ZhangMingzhe
Mr. Beijing University of Chemical Technology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
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09月30日 2024
初稿截稿日期
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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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