phm
There are 17 repositories under phm topic.
hustcxl/Deep-learning-in-PHM
Deep learning in PHM，Deep learning in fault diagnosis，Deep learning in remaining useful life prediction
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）
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预测)
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
kokikwbt/predictive-maintenance
Datasets for Predictive Maintenance
eleGAN23/HyperNets
Hypercomplex Neural Networks with PyTorch
foryichuanqi/RESS-Paper-2022.09-Remaining-useful-life-prediction-by-TaFCN
The source code of paper: Trend attention fully convolutional network for remaining useful life estimation in the turbofan engine PHM of CMAPSS dataset. Signal selection, Attention mechanism, and Interpretability of deep learning are explored.
ritu-thombre99/RUL-Prediction
Remaining useful life estimation of NASA turbofan jet engines using data driven approaches which include regression models, LSTM neural networks and hybrid model which is combination of VAR with LSTM
ericlrf/rul
remaining useful life, residual useful life, remaining life estimation, survival analysis, degradation models, run-to-failure models, condition-based maintenance, CBM, predictive maintenance, PdM, prognostics health management, PHM
hilinxinhui/battery_phm
Algorithms for Battery Mangerment System
foryichuanqi/ADVEI-Paper-2024.3-Degradation-path-approximation-for-remaining-useful-life-estimation
Remaining useful life prediction. Degradation path approximation (DPA) is a highly easy-to-understand and brand-new solution way for data-driven RUL prediction. Many research directions on DPA can be further studied.
adam-aalah/Feature-clustering-and-XAI-for-RUL-estimation
Feature clustering and XIA for RUL estimation
zxuuuustupid/PRN-method
Proposed a deep learning PRN method for Beijing PHM conference.
minelabwot/minelabwot.github.io
Industrial AI Group @ School of Information and Communication Engineering, BUPT
sli1989/CXL_Notes
Study Notes