This is our implemented source code for the paper "Mixmamba-fewshot: mamba and attention mixer-based method with few-shot learning for bearing fault diagnosis" published in the Journal of Applied I…

ChatGPT 中文调教指南。各种场景使用指南。学习怎么让它听你的话。

This repo includes ChatGPT prompt curation to use ChatGPT and other LLM tools better.

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)

SQ dataset for fault diagnosis pulished by Xi'an jiaotong University

The deep residual shrinkage network is a variant of deep residual networks.

计算机自学指南

🍀 Pytorch implementation of various Attention Mechanisms, MLP, Re-parameter, Convolution, which is helpful to further understand papers.⭐⭐⭐

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

A PyTorch Library for Meta-learning Research

Pytorch implementation of Conditional Image Synthesis with Auxiliary Classifier GANs

A simple feature-based time series classifier using Kolmogorov–Arnold Networks

A collection of AWESOME things about GAN

A collection of AWESOME things about domain adaptation

A few shot learning repository for bearing fault diagnosis.

Implementation of the model-agnostic meta-learning framework on CWRU bearing fault dataset to address cross-domain few-shot fault diagnosis problem.

This repository is for the Few-shot Learning for the fault diagnosis of large industrial equipment.

This is the corresponding repository of paper Limited Data Rolling Bearing Fault Diagnosis with Few-shot Learning

Diagnostic and Pronostic in Machine Health Monitoring

Random convolution layer: An auxiliary method to improve fault diagnosis performance

Official repository for the paper "Few‐shot multiscene fault diagnosis of rolling bearing under compound variable working conditions"

An optimizer that trains as fast as Adam and as good as SGD.

A Library for Advanced Deep Time Series Models for General Time Series Analysis.

A Library for Advanced Deep Time Series Models.

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

The PyTorch version for Semi-supervised meta-learning networks with squeeze-and-excitation attention for few-shot fault diagnosis.

Few-shot Transfer Learning for Intelligent Fault Diagnosis of Machine

TICNN

Source codes for the paper "Few-shot bearing fault diagnosis based on meta-learning with discriminant space optimization" (MLDSO) which published in MST