chendingliang

Bayesian-Neural-Networks

Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more

HIconstruct_optimize

Contains code to compare several health index construction methods using run-to-failure bearing dataset

HNUIDG-Fault-Diagnosis-

The intelligent fault diagnosis of HNU IDG

MACNN

Multi-scale Attention Convolutional Neural Network for Time Series Classification

ML_Notes

机器学习算法的公式推导以及numpy实现

projectRUL

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

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.

Remaining-Useful-Life-Prediction-for-Turbofan-Engines

RUL prediction for Turbofan Engine (CMAPSS dataset) using CNN

weibull-knowledge-informed-ml

Using knowledge-informed machine learning on the PRONOSTIA (FEMTO) and IMS bearing data sets. Predict remaining-useful-life (RUL).

Wind_Turbine_SCADA_open_data

list of open data wind turbine data sets

chendingliang/Hyperparameter-Tuning-In-LSTM-Network

Hyperparameter Tuning in LSTM using Genetic Algorithm, Bayesian Optimization, Random Search, Grid Search.

chendingliang/projectRUL

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

chendingliang/Remaining-Useful-Life-Prediction-for-Turbofan-Engines

RUL prediction for Turbofan Engine (CMAPSS dataset) using CNN

chendingliang/BetaLSTM

chendingliang/convlstmgru

Pytorch implementations of ConvLSTM and ConvGRU modules with examples

chendingliang/Deep-Residual-Shrinkage-Networks

The deep residual shrinkage network is a variant of deep residual networks.

chendingliang/DeepLearning

chendingliang/Few-shot-Learning-for-Fault-Diagnosis

chendingliang/GMFE

Generalized Multiscale Feature Extraction for Remaining Useful Life Prediction of Bearings with Generative Adversarial Networks

chendingliang/google-research

Google Research

chendingliang/GraphNeuralNetwork

《深入浅出图神经网络：GNN原理解析》配套代码

chendingliang/handson-ml2

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

chendingliang/KATE

Code & data accompanying the KDD 2017 paper "KATE: K-Competitive Autoencoder for Text"

chendingliang/keras-denoising-autoencoder

Keras Denoising Autoencoder

chendingliang/LSTM

基于LSTM神经网络的时间序列预测

chendingliang/lstm-bayesian-optimization-pytorch

Bayesian Optimization implementation for text classifiction

chendingliang/mc-lstm

Experiments with Mass Conserving LSTMs

chendingliang/Meta-Learning4FSTSF

Meta-Learning for Few-Shot Time Series Forecasting

chendingliang/NASA-Jet-Engine-RUL-Prediction-Notebook

Prediction of Remaining Useful Life (RUL) of NASA Turbofan Jet Engine using libraries such as Numpy, Matplotlib and Pandas. Prediction is done by training a model using Keras (TensorFlow).

chendingliang/PCCNN

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

chendingliang/Probabilistic_RUL_Prediction

chendingliang/Pytorch-Transfomer

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

chendingliang/PyTorch-VAE

A Collection of Variational Autoencoders (VAE) in PyTorch.

chendingliang/Remaining-Useful-Life-Prediction-RNN

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

chendingliang/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

chendingliang/tensor2tensor

Library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research.

chendingliang/TensorFlow2x_Engineering_Implementation

chendingliang/Transfer-Learning-for-Fault-Diagnosis

This repository is for the transfer learning or domain adaptive with fault diagnosis.

chendingliang/transformer

Implementation of Transformer model (originally from Attention is All You Need) applied to Time Series.

chendingliang/transformer-time-series-prediction

proof of concept for a transformer-based time series prediction model