1D-CNN Vibration Signal Bearing Fault Diagnosis

2017工业大数据 风机叶片预测

This is a program of a new weakly supervised learning based oversampling framework to solve the imbalanced classification proposed by Min Qian and Yanfu Li. Reference paper：A weakly supervised lear…

This is an implementation of single source multiple target domain adaptation for fault diagnosis

A collection of AWESOME things about domian adaptation

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名校公开课程评价网

Code release for "Conditional Adversarial Domain Adaptation" (NIPS 2018)

Experiments with supervised contrastive learning methods with different loss functions

中文版 v2 课程

Deep learning in PHM，Deep learning in fault diagnosis，Deep learning in remaining useful life prediction

A collection of implementations of deep domain adaptation algorithms

毕设研究课题：根据轴承的振动序列数据来诊断轴承故障。

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

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

This python script developed approach which uses various Explainable AI techniques to interpret the results given by fault detection and diagnosis model for Air Handling Units.

Wind turbine fault detection using one class SVM

基于深度学习的滚动轴承故障诊断方法

为ChatGPT/GLM提供图形交互界面，特别优化论文阅读润色体验，模块化设计支持自定义快捷按钮&函数插件，支持代码块表格显示，Tex公式双显示，支持Python和C++等项目剖析&自译解功能，PDF/LaTex论文翻译&总结功能，支持并行问询多种LLM模型，支持清华chatglm等本地模型。兼容复旦MOSS, llama, rwkv, 盘古, newbing, claude等

PyTorch code for the paper "Curriculum Graph Co-Teaching for Multi-target Domain Adaptation" (CVPR2021)

ICLR 2021, Contrastive Learning with Hard Negative Samples

Dataset that was used during the IEEE PHM 2012 Data Challenge, built by the FEMTO-ST Institute

机器学习背景下旋转机械振动信号故障诊断是否需要信号预处理——使用CWRU数据的一次尝试 Whether signal preprocessing is needed for fault diagnosis of rotating machinery vibration signals in the context of machine learning - an attempt using …

"In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning" by Mamshad Nayeem Rizve, Kevin Duarte, Yogesh S Rawat, Mubarak Shah (ICLR 2021)

2017工业大数据创新竞赛/风机叶片结冰预测大赛

智能故障诊断和寿命预测期刊（Journals of Intelligent Fault Diagnosis and Remaining Useful Life）

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

Main repository for the NREL-supported OpenFAST whole-turbine and FAST.Farm wind farm simulation codes.

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