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The implementation of "TimesURL: Self-supervised Contrastive Learning for Universal Time Series Representation Learning"
[NeurIPS 2022] SemiFL: Semi-Supervised Federated Learning for Unlabeled Clients with Alternate Training
PyTorch Implementation of Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach
In this project we will builde federated learning application for Predictive maintenance using the NASA/IMS,CWRU and FEMTO datasets
Papers related to Federated Learning in all top venues
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预测)
This is the code of the paper "Federated Transfer Learning for EEG Signal Classification" published in IEEE EMBS 2020 (42nd Annual International Conferences of the IEEE Engineering in Medicine and …
A PyTorch Implementation of Federated Learning
Sensor Fault Diagnosis with Physics Informed Transfer Learning
[IEEE TIM] This is official code for paper "Few-Shot Bearing Fault Diagnosis via Ensembling Transformer-based Model with Mahalanobis Distance Metric Learning from Multiscale Features". IEEE Transac…
Variance discrepancy representation: A vibration characteristic-guided distribution alignment metric for fault transfer diagnosis
Source codes for the paper "Applications of Unsupervised Deep Transfer Learning to Intelligent Fault Diagnosis: A Survey and Comparative Study" published in TIM
AAAI 2025: BearLLM: A Prior Knowledge-Enhanced Bearing Health Management Framework with Unified Vibration Signal Representation
[ICLR 2023 Spotlight] Vision Transformer Adapter for Dense Predictions
Bearing Meta-Learning을 이용한 RUL 예측 논문 구현
Remaining useful life prediction by Transformer-based Model
Implementation of GCU-Transformer for RUL Prediction on CMAPSS
Bearing fault diagnosis model based on MCNN-LSTM
This repository is for studying a trajectory prediction using Kalman filter and deep learning models.
Incorporating Transformer and LSTM to Kalman Filter with EM algorithm
Baseline Kalman models for trajectory predictions. One is constant velocity LSTM
PyTorch implementation of CNN for remaining useful life prediction. Inspired by Babu, G. S., Zhao, P., & Li, X. L. (2016, April). Deep convolutional neural network-based regression approach for est…
Transformer implementation with PyTorch for remaining useful life prediction on turbofan engine with NASA CMAPSS data set. Inspired by Mo, Y., Wu, Q., Li, X., & Huang, B. (2021). Remaining useful l…