Bearing fault diagnosis using transfer learning (or domain adaptation).

A transfer learning fault diagnosis repository covering popular algorithms

Transfer Learning Library for Domain Adaptation, Task Adaptation, and Domain Generalization

A professionally curated list of awesome resources (paper, code, data, etc.) on transformers in time series.

一种轻量化故障诊断框架——LiConvFormer

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Awesome things about domain generalization, including papers, code, etc.

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。

Graph Neural network for Fault Diagnosis

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。

Python codes “Jupyter notebooks” for the paper entitled "A Hybrid Method for Condition Monitoring and Fault Diagnosis of Rolling Bearings With Low System Delay, IEEE Trans. on Instrumentation and M…

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1DCNN Fault Detection（1DCNN的轴承故障诊断）

Implementation of categorical generative adversarial networks for unsupervised bearing fault diagnostics

Source codes for the paper "Deep Learning Algorithms for Rotating Machinery Intelligent Diagnosis: An Open Source Benchmark Study"

This code is about the implementation of Domain Adversarial Graph Convolutional Network for Fault Diagnosis Under Variable Working Conditions.