Deep learning in PHM，Deep learning in fault diagnosis，Deep learning in remaining useful life prediction

智能故障诊断和寿命预测期刊（Journals of Intelligent Fault Diagnosis and Remaining Useful Life）

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)

Datasets for Predictive Maintenance

基于注意力机制的少量样本故障诊断 pytorch

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

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

Hypercomplex Neural Networks with PyTorch

Feature clustering and XIA for RUL estimation

Algorithms for Battery Mangerment System

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.

Industrial AI Group @ School of Information and Communication Engineering, BUPT

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.

Study Notes

Proposed a deep learning PRN method for Beijing PHM conference.