Alternatives and similar repositories for DL-based-Intelligent-Diagnosis-Benchmark

Users that are interested in DL-based-Intelligent-Diagnosis-Benchmark are comparing it to the libraries listed below

• ZhaoZhibin / UDTL
  Source codes for the paper "Applications of Unsupervised Deep Transfer Learning to Intelligent Fault Diagnosis: A Survey and Comparative …
  ☆499Updated 3 years ago
• liguge / Journals-of-Prognostics-and-Health-Management
  智能故障诊断和寿命预测期刊（Journals of Intelligent Fault Diagnosis and Remaining Useful Life）
  ☆409Updated 4 months ago
• AaronCosmos / wdcnn_bearning_fault_diagnosis
  wdcnn轴承故障模型
  ☆378Updated 7 years ago
• hustcxl / Deep-learning-in-PHM
  Deep learning in PHM，Deep learning in fault diagnosis，Deep learning in remaining useful life prediction
  ☆459Updated 4 years ago
• SNBQT / Limited-Data-Rolling-Bearing-Fault-Diagnosis-with-Few-shot-Learning
  This is the corresponding repository of paper Limited Data Rolling Bearing Fault Diagnosis with Few-shot Learning
  ☆361Updated 3 years ago
• Feaxure-fresh / TL-Fault-Diagnosis-Library
  Fault Diagnosis Employing Transfer Learning Techniques: Domain Adaptation and Domain Generalization
  ☆499Updated last year
• CHAOZHAO-1 / DG-PHM
  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…
  ☆344Updated 3 months ago
• Xiaohan-Chen / bear_fault_diagnosis
  Bearing fault diagnosis model based on MCNN-LSTM
  ☆375Updated 2 years ago
• hustcxl / Rotating-machine-fault-data-set
  Open rotating mechanical fault datasets (开源旋转机械故障数据集整理)
  ☆1,095Updated 4 months ago
• cathysiyu / Mechanical-datasets
  ☆279Updated 7 years ago
• mingzhangPHD / Transfer-Learning-for-Fault-Diagnosis
  This repository is for the transfer learning or domain adaptive with fault diagnosis.
  ☆272Updated 2 years ago
• Xiaohan-Chen / transfer-learning-fault-diagnosis-pytorch
  A transfer learning fault diagnosis repository covering popular algorithms
  ☆312Updated last year
• ddrrrr / projectRUL
  to prediction the remain useful life of bearing based on 2012 PHM data
  ☆297Updated 4 years ago
• zhangjiali1201 / Keras_bearing_fault_diagnosis
  ☆212Updated 6 years ago
• wangfin / 1DCNN_Fault_Detection
  1DCNN Fault Detection（1DCNN的轴承故障诊断）
  ☆183Updated 3 years ago
• liguge / Fault-diagnosis-for-small-samples-based-on-attention-mechanism
  基于注意力机制的少量样本故障诊断 pytorch
  ☆251Updated 4 months ago
• beifengchuizoudemeng / CWRU
  ☆112Updated 6 years ago
• ZhangWei1993 / Mechanical-Fault-Diagnosis-Based-on-Deep-Learning
  CNN for mechanical fault diagnosis
  ☆318Updated 7 years ago
• HazeDT / PHMGNNBenchmark
  this code library is mainly about applying graph neural networks to intelligent diagnostic and prognostic.
  ☆291Updated 2 years ago
• phoenixdyf / Opensource_Dataset_Compilation_for_PHM
  分享的所有数据集均为开源的PHM（Prognostics and Health Management）数据，涵盖故障诊断、健康评估和寿命预测等领域。
  ☆211Updated last year
• songyer / Fault_Diagnosis
  基于深度学习的滚动轴承故障诊断方法
  ☆202Updated 6 years ago
• liguge / liguge
  ☆17Updated 2 weeks ago
• HazeDT / DAGCN
  This code is about the implementation of Domain Adversarial Graph Convolutional Network for Fault Diagnosis Under Variable Working Condit…
  ☆173Updated 4 years ago
• BBT0524 / diagnose_fault_by_vibration
  毕设研究课题：根据轴承的振动序列数据来诊断轴承故障。
  ☆131Updated 4 years ago
• zggg1p / A-Domain-Adaption-Transfer-Learning-Bearing-Fault-Diagnosis-Model-Based-on-Wide-Convolution-Deep-Neu
  Inspired by the idea of transfer learning, a combined approach is proposed. In the method, Deep Convolutional Neural Networks with Wide …
  ☆141Updated 7 months ago
• e62505 / Fault_diognosis_dataset
  ☆86Updated 3 years ago
• liguge / Deep-Residual-Shrinkage-Networks-for-intelligent-fault-diagnosis-DRSN-
  Deep Residual Shrinkage Networks for Intelligent Fault Diagnosis（pytorch） 深度残差收缩网络应用于故障诊断
  ☆231Updated 2 years ago
• Litchiware / cwru
  ☆173Updated 4 years ago
• Lucky-Loek / ieee-phm-2012-data-challenge-dataset
  Dataset that was used during the IEEE PHM 2012 Data Challenge, built by the FEMTO-ST Institute
  ☆148Updated 5 years ago
• TxgStars / wdcnn
  实现的是WDCNN的pytorch版本代码，对应论文的第三章 data包含了四个数据文件夹，这里只使用了0HP文件夹中的数据，里面包含了正常、内圈、外圈、滚动体共10种状态 preprocess.py的功能是对数据进行采样、编码，虽然划分出来了验证集但是并没有使用 trai…
  ☆24Updated 2 years ago