采用一种包含加权水平可见图(WHVG)的图卷积网络(GCN)，对采样的轴承震动时间序列数据分析，进行滚动轴承故障诊断。其中，对HVG中两节点的边，以节点距离的倒数作为权重进行加权，以削弱噪声节点对其他距离较远节点的影响。

Repository containing the code for the experiments and examples of my Bachelor Thesis: Cross Domain Fault Detection through Optimal Transport

Adv-IFD: Adversarial Attack Datasets for An Intelligent Fault Diagnosis

This repo provides source code for cross-domain machine fault diagnosis using an unsupervised domain adaptation approach (Feature Representation Alignment Networks).

this code library is mainly about applying graph neural networks to intelligent diagnostic and prognostic.

The source codes of Meta-learning for few-shot cross-domain fault diagnosis.

Implementation of categorical generative adversarial networks for unsupervised bearing fault diagnostics

Pytorch implementation of the paper: "Conditional Adversarial Domain Generalization With a Single Discriminator for Bearing Fault Diagnosis"

智能故障诊断中一维类梯度激活映射可视化展示 1D-Grad-CAM for interpretable intelligent fault diagnosis