Semi-supervised fault diagnosis using GSSL-GraphSage under limited labeled samples
CAO Jie
WANG Ting-yi
WANG Jin-hua
Abstract：In view of the limited labelled monitoring data collected in actual engineering and insufficient label informa-tion mining problem of gearbox fault diagnosis method based on Graph neural network,a Graph-based semi-supervised learning(GSSL)under limited labelled samples is proposed.GSSL and Graph sample and aggregate(GraphSage)algo-rithm for gearbox semi-supervised fault diagnosis.Based on the K-nearest neighbor algorithm and the graph-based label propagation strategy,the label information is propagated to neighborhood samples with similar distribution along the edge,so as to make full use of the label information of limited samples and improve the model performance.Each vibration spectrum sample was regarded as a node to construct a graph-based semi-supervised learning framework.Finally,the semi-supervised learning framework was input into the node-level GraphSage network for fault classification,avoiding the situation of new nodes being retrained,which could effectively prevent the over-fitting of training and enhance the gener-alization ability.The proposed method is used to analyze the experimental data of gearbox faults.The results show that the proposed method can accurately diagnose different fault modes of gearbox under the condition of 6％low label,which verifies the feasibility and effectiveness of gearbox fault diagnosis.
Keywords：fault diagnosisGraphSage networklimited labeled samplessemi-supervised learninglabel propagation strategy
Publication Date：2025-05-30
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：11( 892-902 )