xiaozhangxiaoshi

xiaozhangxiaoshi's Stars

• KeiLongW/battery-state-estimation

  Estimation of the State of Charge (SOC) of Lithium-ion batteries using Deep LSTMs.

  Language:Jupyter Notebook11025

• GuoHaoren/Implementation-of-GCU-Transformer-for-RUL-Prediction-on-CMAPSS

  Implementation of GCU-Transformer for RUL Prediction on CMAPSS

  Language:Python272

• huggingface/transformers

  🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

  Language:Python132k26.3k

• rdbraatz/data-driven-prediction-of-battery-cycle-life-before-capacity-degradation

  Code for Nature energy manuscript

  Language:Jupyter Notebook472192

• zangzelin/Auto-encoder-AE-SAE-DAE-CAE-DAE-with-keras-in-Mnist-and-report

  Language:Python4610

• HinokiBAI/NASA_Li-ion_Battery_SOH_Prediction_with_MVIP-Trans

  This research provides a prognostic framework for off-line SOH estimation of Li-ion battery. With a CNN-Transformer architecture, this program is capable of modeling the temporal correlations of battery signals from both local and global views. In this way, the learning ability of both local features and long period contexts will be enhanced.

  Language:Python659

• dangne/multi-channel-transformer

  Multi-channel Transformer for battery remaining useful life prediction

  Language:Python156

• zacchen14/RUL_GLIN

  GLIN: Remaining useful life prediction based on fusion of global and local information (Transformer)

  Language:Python281

• Junkun-Lu/RUL_Inception-Attention

  Remaining useful life prediction by Transformer-based Model

  Language:Python364

• survml/survml-transformer-rul-prediction

  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 life estimation via transformer encoder enhanced by a gated convolutional unit. Journal of Intelligent Manufacturing, 1-10.

  Language:Python221

• jiaxiang-cheng/PyTorch-Transformer-for-RUL-Prediction

  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 life estimation via transformer encoder enhanced by a gated convolutional unit. Journal of Intelligent Manufacturing, 1-10.

  Language:Python21648

• mohyunho/MOO_ELM

  Multi-Objective Optimization of ELM for RUL Prediction

  Language:Python142

• mohyunho/NAS_transformer

  Evolutionary Neural Architecture Search on Transformers for RUL Prediction

  Language:Python386

• xxl4tomxu98/NASA-Jet-Engine-Maintenance

  ML Approaches for RUL Prediction, Anomaly Detection, Survival Analysis and Failure Classification

  Language:Jupyter Notebook275

• 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.

  Language:Python482

• zhmou/Turbofan-engine-RUL-prediction

  RUL prediction for C-MAPSS dataset, reproduction of this paper: https://personal.ntu.edu.sg/xlli/publication/RULAtt.pdf

  Language:Python8511

• mohyunho/N-CMAPSS_DL

  N-CMAPSS data preparation for Machine Learning and Deep Learning models. (Python source code for new CMAPSS dataset)

  Language:Jupyter Notebook729

• Abhijit-Bhumireddy99/RUL_Prediction

  remaining Useful Life (RUL) Prediction of Mechanical Bearings using Continuous Wavelet Transform (CWT), Convolution Neural Network (CNN), and Long Short Term Memory (LSTM) unit

  Language:Jupyter Notebook11816

• fmardero/battery_aging

  NASA Li-ion Battery Aging Datasets

  Language:Jupyter Notebook226

• SymposiumOrganization/Dynaformer

  Implementation, data and pretrained models for the paper "Dynaformer: A Deep Learning Model for Ageing-aware Battery Discharge Prediction"

  Language:Python241

• MichaelBosello/battery-rul-estimation

  Remaining Useful Life (RUL) estimation of Lithium-ion batteries using deep LSTMs

  Language:Jupyter Notebook16140