There are 34 repositories under remaining-useful-life topic.
Deep learning approach for estimation of Remaining Useful Life (RUL) of an engine
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.
Remaining Useful Life (RUL) estimation of Lithium-ion batteries using deep LSTMs
This repository contains code that implement common machine learning algorithms for remaining useful life (RUL) prediction.
Using knowledge-informed machine learning on the PRONOSTIA (FEMTO) and IMS bearing data sets. Predict remaining-useful-life (RUL).
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.
Datasets for Predictive Maintenance
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.
RUL prediction for C-MAPSS dataset, reproduction of this paper: https://personal.ntu.edu.sg/xlli/publication/RULAtt.pdf
Tool wear prediction by residual CNN
N-CMAPSS data preparation for Machine Learning and Deep Learning models. (Python source code for new CMAPSS dataset)
Predictive Maintenance System for Digital Factory Automation
False Data Injection Attacks in Internet of Things and Deep Learning enabled Predictive Analytics
This project is about predictive maintenance with machine learning. It's a final project of my Computer Science AP degree.
Bearing remaining useful life prediction using support vector machine and hybrid degradation tracking model - Implementation of Research Paper : https://doi.org/10.1016/j.isatra.2019.08.058
A collection of datasets for RUL estimation as Lightning Data Modules.
CeRULEo: Comprehensive utilitiEs for Remaining Useful Life Estimation methOds
This is a repository of sample codes and implementation framework for industrial machine predictive maintenance tasks using deep learning models.
Remaining Useful Life (RUL) prediction for Turbofan Engines
Remaining useful life estimation of NASA turbofan jet engines using data driven approaches which include regression models, LSTM neural networks and hybrid model which is combination of VAR with LSTM
Remaining Useful Life estimation and sensor data generation by VAE and diffusion model on C-MAPSS dataset.
ProFeld: survival analysis, predictive maintenance, churn analysis, and remaining useful life prediction in Python
RNN-flavoured Ensembling to Predict Remaining Useful Life of Lithium-ion Batteries
Bayesian Deep Learning for Remaining Useful Life Estimation of Machine Tool Components
This repository holds the results of a project on Remaining Useful Lifetime estimation of a turbofan engine for a course of Delft University of Technology.
Conformal Prediction Intervals for Remaining Useful Lifetime Estimation (IJPHM 2023)
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).