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Name: Guang Yang
Type: User
Company: China University of Geosciences
Bio: Graduate student, College of Mechanical and Electrical Engineering, China University of Geosciences (Wuhan).
Location: wuhan
Name: Guang Yang
Type: User
Company: China University of Geosciences
Bio: Graduate student, College of Mechanical and Electrical Engineering, China University of Geosciences (Wuhan).
Location: wuhan
2018 phm data challenge, ion mill machine RUL & fault diagnosis
深度学习近年来关于神经网络模型解释性的相关高引用/顶会论文(附带代码)
Deep learning in PHM,Deep learning in fault diagnosis,Deep learning in remaining useful life prediction
Survival analsyis and time-to-failure predictive modeling using Weibull distributions and Recurrent Neural Networks in Keras
A collection of various deep learning architectures, models, and tips
Remaining Useful Life prediction with a Deep Self-Supervised Learning Approach
Contains code to compare several health index construction methods using run-to-failure bearing dataset
Demo Weibull Time-to-event Recurrent Neural Network in Keras
code repository for the owner's paper on prognostics
to prediction the remain useful life of bearing based on 2012 PHM data
Autoencoders in PyTorch
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.
Deep learning approach for estimation of Remaining Useful Life (RUL) of an engine
This repository contains code that implement common machine learning algorithms for remaining useful life (RUL) prediction.
Application of survival analysis, predictive maintenance, churn analysis, remaining useful life prediction
COX Proportional risk model and survival analysis implemented by tensorflow.
A PyTorch implementation of Weibull Time to Event Recurrent Neural Networks for churn prediction tasks.
Using knowledge-informed machine learning on the PRONOSTIA (FEMTO) and IMS bearing data sets. Predict remaining-useful-life (RUL).
WTTE-RNN a framework for churn and time to event prediction
Weibull Time To Event prediction with PyTorch and deep learning
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