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Open rotating mechanical fault datasets (开源旋转机械故障数据集整理)
Free and open-source software that allows users to set animated desktop wallpapers and screensavers powered by WinUI 3.
这是论文Unsupervised Domain Adaptation by Backpropagation的复现代码，并完成了MNIST与MNIST-M数据集迁移，master和tf2分支代码为是基于tf2.x，tf1分支代码基于tf1.x
这是一个首层卷积为宽卷积的深度神经网络Deep Convolutional Neural Networks with Wide First-layer Kernels (WDCNN)的实现，该模型具有优越的抗噪能力，可用于轴承的智能故障诊断。
This is the corresponding repository of paper Limited Data Rolling Bearing Fault Diagnosis with Few-shot Learning
Convolutional Neural Network for 3D meshes in PyTorch
Open dataset in the field of mechanical fault diagnosis under variable speed conditions, providing benchmark for algorithm performance evaluation
[RESS 2022] Code for Dual adversarial network for cross-domain open set fault diagnosis
[ISA Transactions] A balanced and weighted alignment network for partial transfer fault diagnosis
This is a benckmark for domain generalization-based fault diagnosis （基于领域泛化的相关代码）
Open rotating mechanical fault datasets (开源旋转机械故障数据集整理)
This is an open source lib called "DGFDBenchmark" for domain-generalization-based fault diagnosis
VMD-SWTTV是一种针对一维信号的二级框架降噪算法，结合了变分模态分解VMD与平稳小波变换SWT，并采用了小波变换全变分法优化了SWT。降噪效果还是不错的。
this code implements the Bayesian-MCMC based prognostics model. PHM2010 data challenge data are used to verifty the model.
[MSSP 2023] Mutual-assistance semisupervised domain generalization network for intelligent fault diagnosis under unseen working conditions
[RESS 2022] Adaptive open set domain generalization network: Learning to diagnose unknown faults under unknown working conditions
This is a reposotory that includes paper、code and datasets about domain generalization-based fault diagnosis and prognosis. (基于领域泛化的故障诊断和预测)
根据单个或多个宽频偶数长度电磁信号，根据VMD计算分量数据，展示含噪曲线、去噪曲线、分量时域、分量频谱、多测道曲线的图形绘制和数据生成。实现单条信号的独立运算，多条信号可选参数的独立运算并实现合并。 源码中包含一组测试数据，结果与matlab一致。
Implementation of R-GCNs for Relational Link Prediction
Keras-based implementation of Relational Graph Convolutional Networks
Graph Attention Networks (https://arxiv.org/abs/1710.10903)
Implementation of Graph Convolutional Networks in TensorFlow