chendingliang
I am currently working toward the PhD degree in the School of Mechanical and Vehicle Engineering, Chongqing University
Chongqing UniversityShapingba District, Chongqing
Pinned Repositories
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's Repositories
chendingliang/autoencoder
个人练习，自编码器及其变形（理论+实践）
chendingliang/Basic-Rotating-Machine-Vibration-Analysis
These codes realize data transformation and simple data processing for fault diagnosis.
chendingliang/cnn-convlstm-time-series
Inspired by the success and computational efficiency of convolutional architectures for various sequential tasks compared to recurrent neural networks. We explored CNN and RCNN autoencoder whose representations can be utilized for the task of time-series classification. Our results surpass existing RNN and DTW-based-classifiers on 11 out of 30 datasets while the existing RNN achieved 8/30.
chendingliang/cnn-lstm-bilstm-deepcnn-clstm-in-pytorch
In PyTorch Learing Neural Networks Likes CNN(Convolutional Neural Networks for Sentence Classification (Y.Kim, EMNLP 2014) 、LSTM、BiLSTM、DeepCNN 、CLSTM、CNN and LSTM
chendingliang/ConvRNN_for_RUL_estimation
Code used in Thesis "Convolutional Recurrent Neural Networks for Remaining Useful Life Prediction in Mechanical Systems".
chendingliang/DeepLearnToolbox
Matlab/Octave toolbox for deep learning. Includes Deep Belief Nets, Stacked Autoencoders, Convolutional Neural Nets, Convolutional Autoencoders and vanilla Neural Nets. Each method has examples to get you started.
chendingliang/DLwithPyTorch
Code to accompany my upcoming book "Deep learning with PyTorch Book " from Packt
chendingliang/g2-lstm
Codes for "Towards Binary-Valued Gates for Robust LSTM Training".
chendingliang/Gated-Recurrent-Unit-GRU
An implementation of Gated Recurrent Unit
chendingliang/Gaussian-mixture-model
Implementation of Gaussian mixture model using expectation maximization (EM), variational inference (VI), and Gibbs sampler (GS).
chendingliang/GaussianMixtureModel
Different implementations of gaussian mixture model (EM, Variational, MCMC)
chendingliang/gmm-em-clustering
高斯混合模型（GMM 聚类）的 EM 算法实现。
chendingliang/GMM_CAVI
Coordinate ascent variational inference for a Gaussian mixture model
chendingliang/GRU_implementation
Gated Recurrent Unit implementation from scratch
chendingliang/hipsternet
All the hipster things in Neural Net in a single repo
chendingliang/igmm
Infinite Gaussian Mixture Model
chendingliang/machine-failure-detection
PCA and DBSCAN based anomaly and outlier detection method for time series data.
chendingliang/MATLAB-GRU
Gated Recurrent Unit implementation in MATLAB
chendingliang/MCMC
Collection of Monte Carlo (MC) and Markov Chain Monte Carlo (MCMC) algorithms applied on simple examples.
chendingliang/models
Models built with TensorFlow
chendingliang/Nested-LSTM-NLSTM-Pytorch
NLSTM Nested LSTM in Pytorch
chendingliang/predictive-maintenance-lstm
Predicting the Remaining Useful Life (RUL) of simulated turbofan data using Keras and LSTM.
chendingliang/pytorch-tutorial
PyTorch Tutorial for Deep Learning Researchers
chendingliang/pytorch_convgru
Convolutional Gated Recurrent Units implemented in PyTorch
chendingliang/rul_using_CNN_LSTM
Predict remaining useful life of a machine from it's historical data using CNN and LSTM
chendingliang/Sparse-AutoEncoder
稀疏自编码器
chendingliang/SpeechSignalProcessingCourse
语音信号处理实验教程（MATLAB源代码）
chendingliang/tensorflow-Deep-learning
Tensorflow Examples
chendingliang/tensorflow_stacked_denoising_autoencoder
Implementation of the stacked denoising autoencoder in Tensorflow
chendingliang/Wiener-Degradation-Models
The data are processed