Prediction of Remaining Useful Life(RUL) for Aircraft Engine Using Neural Network Models

The project focuses on prediction of RUL (Remaining Useful Life) of aircraft engine. The acitivity is carried out in PyTorch frameowrk using at first a simple feedforward neural network, followed by 1D convolutional neural network based model from scratch.

Addresses the problem of remaining useful life (RUL) prediction in a fleet of aircraft engines under conditions of high variability in the flight envelope and multiple failure modes.

This project predicts the Remaining Useful Life of aircraft engines using the NASA C-MAPSS dataset. Multiple machine learning and deep learning models (Linear Regression, Random Forest, SVR, and CNN+LSTM) are compared for time-series sensor data, with emphasis on feature engineering and real-world predictive maintenance applications.

Predict Remaining Useful Life (RUL) of aircraft engines using deep learning on the NASA CMAPSS dataset. This project implements LSTM-based neural networks, sliding window preprocessing, and evaluation metrics with visualization of predictions and errors.

ML model for aircraft engine Remaining Useful Life (RUL) prediction using NASA CMAPSS dataset

Developed an ML-based Remaining Useful Life (RUL) prediction system for aircraft engines, achieving performance comparable to deep learning models while addressing limitations such as computational complexity and lack of interpretability.

End-to-end AI-based predictive maintenance system for aircraft turbofan engines using NASA C-MAPSS sensor data, combining non-linear machine learning models and LSTM deep learning for Remaining Useful Life (RUL) prediction.

Remaining useful life (RUL) prediction within the modules of aircraft gas turbine engines. It is about how damage propagation can be modeled with different approaches. This is part of the "Praktikum: Smart Data Analytics" SS24 at KIT (Karlsruher Institute of Technology).

Remaining useful life (RUL) predictions for aircraft engines (NASA challenge data)

Built a machine learning model to predict aircraft engine RUL using NASA's CMAPSS FD001 dataset for maintenance

Remaining Useful Life (RUL) prediction for aircraft engines using Sensor Signal Processing & Random Forest

Predictive Maintenance of Aircraft Engines Using LSTM Neural Networks for Rolls-Royce Aerospace - Deep Learning Approach for Remaining Useful Life (RUL) Prediction

Remaining Useful Life (RUL) prediction project using NASA dataset. This project applies machine learning techniques for predictive maintenance and failure analysis on aircraft engine data.

Developed a machine learning model to predict Remaining Useful Life (RUL) of aircraft engines using NASA’s CMAPSS dataset. Implemented data preprocessing, feature scaling, and Random Forest regression to achieve accurate predictions and visualized model performance.

Trustworthy aircraft engine Remaining Useful Life (RUL) prediction system using an attention-based model with Physics-Informed mode, Reliability Index gating (ACCEPT/WARN/REJECT), streaming replay, side-by-side Baseline vs PI comparison, and automated ablation reporting.

Predictive Maintenance: TurboFan Engine RUL Prediction using NASA CMAPSS Dataset. This project implements a high-performance machine learning pipeline to predict the Remaining Useful Life (RUL) of aircraft engines. Leveraging XGBoost and advanced feature engineering, the model achieves a competitive RMSE of 17.99 on the FD001 test set.

This project presents an end-to-end predictive maintenance system for aircraft engines using a Transformer-GRU deep learning model. The system estimates the Remaining Useful Life (RUL) of engines from multivariate sensor data and converts predictions into actionable maintenance decisions through an interactive dashboard.

This project develops a predictive maintenance framework for aircraft turbofan engines using NASA's C-MAPSS dataset. It combines machine learning (LightGBM) for Remaining Useful Life (RUL) prediction with discrete-event simulation (SimPy) to model the impact of maintenance queue delays on engine fleet health.

End-to-end AI-driven predictive maintenance system for aircraft turbofan engines using NASA C-MAPSS sensor data. Hybrid CNN-LSTM model achieves 99.15% accuracy (RMSE: 5.27 cycles) for Remaining Useful Life (RUL) prediction, providing 1-day advance failure warning. Compares 7 models