Multi-Objective Optimization of ELM for RUL Prediction
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Updated
Jun 1, 2022 - Python
Multi-Objective Optimization of ELM for RUL Prediction
Evolutionary Neural Architecture Search for Remaining Useful Life Prediction
Feature clustering and XIA for RUL estimation
Remaining Useful Life (RUL) prediction for Turbofan Engines
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).
Evolutionary Neural Architecture Search on Transformers for RUL Prediction
This paper summarizes a deep learning-based approach with an LSTM trained on the widely used Oxford battery degradation dataset and the help of generative adversarial networks (GANS).
RUL prediction for C-MAPSS dataset, reproduction of this paper: https://personal.ntu.edu.sg/xlli/publication/RULAtt.pdf
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
N-CMAPSS data preparation for Machine Learning and Deep Learning models. (Python source code for new CMAPSS dataset)
False Data Injection Attacks in Internet of Things and Deep Learning enabled Predictive Analytics
This repository contains code that implement common machine learning algorithms for remaining useful life (RUL) prediction.
In this project I aim to apply Various Predictive Maintenance Techniques to accurately predict the impending failure of an aircraft turbofan engine.
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