Alternatives and similar repositories for power-quality-disturbances-with-machine-learning:

Users that are interested in power-quality-disturbances-with-machine-learning are comparing it to the libraries listed below

• AaronCosmos / Deal-with-paderborn-bearing-data
  This repository is used to share some works related to Paderborn Bearing Data
  ☆9Updated 3 years ago
• chachkes247 / Power-Quality-Disturbances
  ☆20Updated 4 years ago
• v3551G / Imaging_sequence_data
  ☆23Updated 4 years ago
• Jaehoon9201 / PyTorch-1D-ACGAN-Data-Augmentation
  PyTorch-1D-ACGAN for data generation. It can also work as classfier
  ☆21Updated 3 years ago
• yushi1006 / Subband-averaging-kurtogram-with-dual-tree-complex-wavelet-packet
  Subband averaging kurtogram (SAK), incorporating with dual-tree complex wavelet packet transform (DTCWPT), to improve performance of the …
  ☆12Updated 3 years ago
• lenalee103 / Aggregates-Database-based-on-ACGAN
  improved by ACWGAN-gp
  ☆10Updated 5 years ago
• Fabian0597 / MMD_PHMS
  Master Thesis: Intelligent Ball Screw Fault Diagnosis Using Deep Learning Based Domain Adaptation and Transfer Learning
  ☆36Updated 2 years ago
• jxw2018 / cost-sensitive-learning-for-imbalanced-fault-diagnosis
  imbalanced fault diagnosis
  ☆14Updated 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
  ☆10Updated 6 years ago
• Daipuwei / DANN-MNIST
  这是论文Unsupervised Domain Adaptation by Backpropagation的复现代码，并完成了MNIST与MNIST-M数据集迁移，master和tf2分支代码为是基于tf2.x，tf1分支代码基于tf1.x
  ☆64Updated 3 years ago
• qinenergy / syn2real
  Code and data for our paper on IEEE-TIE: Integrating Expert Knowledge with Domain Adaptation for Unsupervised Fault Diagnosis
  ☆38Updated 2 years ago
• iDestro / Gramian-Angular-Field-in-Pytorch
  A implementation of Gramian Angular Field (GAF) in Pytorch.
  ☆14Updated last year
• zachysun / MACNN-Pytorch
  Unofficial implementation of paper “Multi-scale Attention Convolutional Neural Network for time series classification”[Neural Networks202…
  ☆16Updated 2 years ago
• CSU-IILab / DNN-CCA
  Deep neural network aided canonical correlation analysis (DNN-CCA) in Tensorflow and Keras
  ☆13Updated 3 years ago
• chamwen / DaNN_DJP
  Domain Adaptive Neural Networks with DJP-MMD
  ☆18Updated 3 years ago
• erphm / MA1DCNN
  The code of Understanding and Learning Discriminant Features based on Multi-Attention 1DCNN for Wheelset Bearing Fault Diagnosis.
  ☆24Updated 5 years ago
• whyre788 / GAN-1D
  using WGAN to generate fault bearing vibration signals
  ☆50Updated 6 years ago
• Botao-An / AAU_Net
  The code for paper "Adversarial Algorithm Unrolling Network for Interpretable Mechanical Anomaly Detection"
  ☆20Updated 2 years ago
• azollanvari / FaultPrognosisDL
  ☆12Updated 4 years ago
• XiubenWu / GANFORCWRU
  New GAN models in dataset of CWRU
  ☆39Updated 3 years ago
• liguge / AMSGradP-for-intelligent-fault-diagnosis
  Imporved AdamP called AMSGradP is suitable for intelligent fault diagnosis (故障诊断)
  ☆11Updated 2 years ago
• XD-onmyway / cnn_for_fault_diagnosis
  ☆26Updated 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 3 years ago
• hectorLop / Conditional-Adversarial-Domain-Generalization-with-Single-Discriminator
  Pytorch implementation of the paper: "Conditional Adversarial Domain Generalization With a Single Discriminator for Bearing Fault Diagnos…
  ☆33Updated 3 years ago
• 123EastGod / Novel-joint-transfer-network
  ☆22Updated 2 years ago
• fyancy / SSMN
  The PyTorch version for Semi-supervised meta-learning networks with squeeze-and-excitation attention for few-shot fault diagnosis.
  ☆56Updated 3 years ago
• TAN-OpenLab / logistic-ELM
  A fast fault diagnosis method for rolling bearings, based on extreme learning machine (ELM) and logistic mapping.
  ☆20Updated 3 years ago
• SY-Ma / Gated-Transformer-on-MTS
  使用改良的Transformer模型应用于多维时间序列的分类任务上
  ☆11Updated 4 years ago
• viniciuserra / Gearbox-Hybrid-CNN-SVM
  The features of nonlinearity and non-stationarity in real systems are often difficult to be extracted. This paper focuses on developing a…
  ☆23Updated last year
• PHM-Code / MWA-CNN
  The code of Interpretable Convolutional Neural Network with Multilayer Wavelet for Noise-Robust Machinery Fault Diagnosis
  ☆40Updated 2 years ago