Alternatives and similar repositories for PyTorch-1D-ACGAN-Data-Augmentation

Users that are interested in PyTorch-1D-ACGAN-Data-Augmentation are comparing it to the libraries listed below

• XiubenWu / GANFORCWRU
  New GAN models in dataset of CWRU
  ☆40Updated 3 years ago
• Botao-An / AAU_Net
  The code for paper "Adversarial Algorithm Unrolling Network for Interpretable Mechanical Anomaly Detection"
  ☆20Updated 2 years ago
• lenalee103 / Aggregates-Database-based-on-ACGAN
  improved by ACWGAN-gp
  ☆10Updated 5 years ago
• carlosfel / Motor-Fault-Diagnosis
  for my thesis work
  ☆17Updated 4 years ago
• BlingBlingss / VAE-CWGAN-GP
  Chiller Fault Diagnosis based on VAE Enabled Generative Adversarial Networks
  ☆44Updated 5 years ago
• LixiangHan / GANs-for-1D-Signal
  implementation of several GANs with pytorch
  ☆159Updated 3 years ago
• he-zh / vibration_gan
  Implement GANs to generate time-series signals for imbalanced learning problem. The experiments are conducted using CWRU bearing data.
  ☆84Updated 3 years ago
• jxw2018 / cost-sensitive-learning-for-imbalanced-fault-diagnosis
  imbalanced fault diagnosis
  ☆14Updated 5 years ago
• kashyaprakshith / Domain_Adaptation_Bosch_Rexroth
  This is an implementation of “Domain Adaptation for Fault Diagnosis on CWRU Dataset"
  ☆14Updated 3 years ago
• opprud / bearing_dataset
  ☆23Updated last year
• binzh93 / fault_diagnosis_cnn_pytorch
  ☆10Updated 6 years ago
• cingtiye / Basic-time-domain-and-frequency-domain-features-of-signals
  ☆33Updated 5 years ago
• susu-cm / bearings-dataset
  Condition Based Maintenance Fault Database for Testing of Diagnostic and Prognostics Algorithms
  ☆16Updated 9 months ago
• lkurakht / CatGAN-bearing-faults
  Implementation of categorical generative adversarial networks for unsupervised bearing fault diagnostics
  ☆58Updated 3 years ago
• TAN-OpenLab / logistic-ELM
  A fast fault diagnosis method for rolling bearings, based on extreme learning machine (ELM) and logistic mapping.
  ☆22Updated 3 years ago
• liguge / 1D-Grad-CAM-for-interpretable-intelligent-fault-diagnosis
  智能故障诊断中一维类梯度激活映射可视化展示 1D-Grad-CAM for interpretable intelligent fault diagnosis
  ☆109Updated 2 years ago
• whyre788 / GAN-1D
  using WGAN to generate fault bearing vibration signals
  ☆51Updated 7 years ago
• mohamedr002 / AMDA
  This is an implementation of single source multiple target domain adaptation for fault diagnosis
  ☆47Updated 3 years ago
• PHM-Code / MWA-CNN
  The code of Interpretable Convolutional Neural Network with Multilayer Wavelet for Noise-Robust Machinery Fault Diagnosis
  ☆44Updated 3 years ago
• erphm / MA1DCNN
  The code of Understanding and Learning Discriminant Features based on Multi-Attention 1DCNN for Wheelset Bearing Fault Diagnosis.
  ☆26Updated 5 years ago
• strongnine / Series2Image
  Encoding time series as images using GAF operation by pyts.
  ☆225Updated 3 years ago
• caiwd / PCCNN
  This is a repository of code and experiment data for paper <A probability confidence CNN model and its application in mechanical fault di…
  ☆10Updated 4 years ago
• albertszg / DFAWnet
  code for DFAWnet
  ☆36Updated 2 years ago
• xaddwell / Adversarial-Attack-on-Machinery-Fault-Diagnosis
  The code for http://arxiv.org/abs/2110.02498
  ☆18Updated 2 years ago
• fboldt / signalProcessing
  Experiments performed to analyse the CNN on the CWRU data set.
  ☆14Updated 5 years ago
• SalieriLu / BearingFaultDiagnostics
  ☆20Updated last year
• WH-Huan / JL-CNN
  The code of Joint Learning CNN for Vibration Signal Denoising and Bearing Fault Diagnosis under Unknown Noise Condition.
  ☆23Updated 4 years ago
• eddardd / CrossDomainFaultDiagnosis
  Repository containing the code for the experiments and examples of my Bachelor Thesis: Cross Domain Fault Detection through Optimal Trans…
  ☆24Updated 2 years ago
• JiahongChen / FRAN
  This repo provides source code for cross-domain machine fault diagnosis using an unsupervised domain adaptation approach (Feature Represe…
  ☆37Updated 4 years ago
• cookieli / Bearing_detect
  利用深度学习自编码器进行故障诊断的程序
  ☆11Updated 7 years ago