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).
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Updated
Apr 14, 2024 - Jupyter Notebook
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).
Feature clustering and XIA for RUL estimation
This repository contains code that implement common machine learning algorithms for remaining useful life (RUL) prediction.
Evolutionary Neural Architecture Search on Transformers for RUL Prediction
N-CMAPSS data preparation for Machine Learning and Deep Learning models. (Python source code for new CMAPSS dataset)
Evolutionary Neural Architecture Search for Remaining Useful Life Prediction
RUL prediction for C-MAPSS dataset, reproduction of this paper: https://personal.ntu.edu.sg/xlli/publication/RULAtt.pdf
In this project I aim to apply Various Predictive Maintenance Techniques to accurately predict the impending failure of an aircraft turbofan engine.
Multi-Objective Optimization of ELM for RUL Prediction
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).
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
False Data Injection Attacks in Internet of Things and Deep Learning enabled Predictive Analytics
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