chendingliang Goto Github PK

nested-lstm-nlstm-pytorch

NLSTM Nested LSTM in Pytorch

neuroai

NeuroAI-UW seminar, a regular weekly seminar for the UW community, organized by NeuroAI Shlizerman Lab.

nndl.github.io

《神经网络与深度学习》 邱锡鹏著 Neural Network and Deep Learning

pccnn

This is a repository of code and experiment data for paper <A probability confidence CNN model and its application in mechanical fault diagnosis>

phmgnnbenchmark

this code library is mainly about applying graph neural networks to intelligent diagnostic and prognostic.

predictive-maintenance-lstm

Predicting the Remaining Useful Life (RUL) of simulated turbofan data using Keras and LSTM.

predictive-maintenance-of-aircraft-engine

In this project I aim to apply Various Predictive Maintenance Techniques to accurately predict the impending failure of an aircraft turbofan engine.

prml

Repository of notes, code and notebooks in Python for the book Pattern Recognition and Machine Learning by Christopher Bishop

prmlt

Matlab code of machine learning algorithms in book PRML

probabilistic_rul_prediction

prognostic_and_rul_prediction_of_bearing_data

One model for RUL and fault prognostic prediction on XJTU bearing dataset

prognosticsalgorithmlibrary

projectrul

to prediction the remain useful life of bearing based on 2012 PHM data

pytorch-cnn-for-rul-prediction

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 estimation of remaining useful life. In International conference on database systems for advanced applications (pp. 214-228). Springer, Cham.

pytorch-course

JULYEDU PyTorch Course

pytorch-lstm-for-rul-prediction

PyTorch implementation of remaining useful life prediction with long-short term memories (LSTM), performing on NASA C-MAPSS data sets. Partially inspired by Zheng, S., Ristovski, K., Farahat, A., & Gupta, C. (2017, June). Long short-term memory network for remaining useful life estimation.

pytorch-siamese

Siamese Network implementation using Pytorch

pytorch-transfomer

My implementation of the transformer architecture from the Attention is All you need paper applied to time series.

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.

pytorch-tutorial

PyTorch Tutorial for Deep Learning Researchers

pytorch-vae

A Collection of Variational Autoencoders (VAE) in PyTorch.

pytorch_convgru

Convolutional Gated Recurrent Units implemented in PyTorch

pytorchintroduction

《深入浅出 PyTorch——从模型到源码》源代码和勘误（见Issues）

remaining-useful-life-estimation-variational

remaining-useful-life-prediction-for-turbofan-engines

RUL prediction for Turbofan Engine (CMAPSS dataset) using CNN

remaining-useful-life-prediction-rnn

Remaining Useful Life Prediction Using RNN/LSTM/GRU Neural Networks

rotating-machine-fault-data-set

Open rotating mechanical fault datasets (开源旋转机械故障数据集整理)

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

rul-and-soh-estimation-of-lithium-ion-satellite-power-systems-using-support-vector-regression

Pipeline to identify remaining useful life of Li-ion batteries using SVR to forecast end of life.

rul-net

Deep learning approach for estimation of Remaining Useful Life (RUL) of an engine