learning-22-code Goto Github PK

atda

Implement of Asymmetric Tri-training for Unsupervised Domain Adaptation

cnn-rnn-ed

use cnn to extracte the feature of vibration raw data,then evaluate HI by rnned

data-science-notes

数据科学的笔记以及资料搜集

gearbox-hybrid-cnn-svm

The features of nonlinearity and non-stationarity in real systems are often difficult to be extracted. This paper focuses on developing a Convolutional Neural Network (CNN) to obtain features directly from the original vibration signals of a gearbox with different pinion conditions. Experimental data is used to show the efficiency of the presented

gmfe

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

hello-world

start to learn how to use github

heuristic-algorithm

启发式算法总结

ieee-phm-2012-data-challenge-dataset

Dataset that was used during the IEEE PHM 2012 Data Challenge, built by the FEMTO-ST Institute

learning-22-code

Config files for my GitHub profile.

lstm-lifetime-estimation

This LSTM is used to predict the rest useful lifetime of ball-bearings. Its programmed with Pytorch and uses the PRONOSTIA Dataset.

openfast

Main repository for the NREL-supported OpenFAST whole-turbine and FAST.Farm wind farm simulation codes.

predict-remaining-useful-life

Predict remaining useful life of a component based on historical sensor observations using automated feature engineering

projectrul

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

projectrul2

pytorch-atda

A PyTorch implementation for Asymmetric Tri-training for Unsupervised Domain Adaptation

remainingusefullife

It is a collection of all kinds of feature extraction and recognition algorithms

rul-from-vibration-signal

Finding lifetime a bearing using IOT data and Remaining Utility Life (RUL)

self-labeled-techniques-for-semi-supervised-learning

semi_boost

time-series-transformers-review

A professionally curated list of awesome resources (paper, code, data, etc.) on transformers in time series.

tri-training

The python implementation of tri-triaing

weibull-knowledge-informed-ml

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

wt-fdd

Wind Turbine Fault Detection. Newer version @ https://github.com/lkev/wtphm

wt_fault_identification

Wind Turbine Fault Identification using Machine Learning Techniques applied to SCADA data

wtphm

SCADA data pre-processing library for prognostics, health management and fault detection of wind turbines. Successor to https://github.com/lkev/wt-fdd