This is the corresponding repository of paper Limited Data Rolling Bearing Fault Diagnosis with Few-shot Learning
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Updated
Oct 10, 2022 - Jupyter Notebook
This is the corresponding repository of paper Limited Data Rolling Bearing Fault Diagnosis with Few-shot Learning
This repository contains data and code that implement common machine learning algorithms for machinery condition monitoring task.
Bearing fault detection public datasets collection.
A comprehensive benchmark of 16 Deep Learning models (including ResNet, LSTM, Transformer, DenseNet) for CWRU Bearing Fault Diagnosis using PyTorch.
🧠 [Signal] Fault-specific vibration signal generation using WGAN-CGAN
Awesome Deep Fault Diagnosis
repo for big data analysis final project (rul prediction + fault classification)
🚨 [Signal] Deep learning-based fault classification of vibration signals using CNN
a bearing fault diagnosis based on deep learning using CNN, Transformer and NoiseAug on the CWRU dataset.
基于 CWRU 真实轴承振动数据的电机故障诊断系统:时频域特征工程 + 随机森林/1D-CNN 双范式对比,含跨负载泛化与抗噪鲁棒性评估 | Bearing fault diagnosis on CWRU vibration data — feature engineering, RandomForest vs 1D-CNN, cross-load & noise-robustness evaluation
A collaborative repo for written assignments issued by Dr. Koyutürk in CSDS 313: Data Analysis during Fall 2020 semester.
A collaborative repo for written assignments issued by Dr. Guo in MATH 307: Linear Algebra during Spring 2021 semester.
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