Remaining Useful Life Prediction for Aircraft Engines using LSTM
This study uses a Long Short-Term Memory (LSTM) network to predict the remaining useful life (RUL) of jet engines from time-series data, crucial for aircraft maintenance and safety. The LSTM model's performance is compared with a Multilayer Perceptron (MLP) on the C-MAPSS dataset from NASA, which contains jet engine run-to-failure events. The LSTM learns from temporal sequences of sensor data, while the MLP learns from static data snapshots. The LSTM model consistently outperforms the MLP in prediction accuracy, demonstrating its superior ability to capture temporal dependencies in jet engine degradation patterns. The software for this project is in https://github.com/AneesPeringal/rul-prediction.git.
Code (1)
Tasks
PredictionTemporal SequencesTime SeriesMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Aircraft Engines Remaining Useful Life Prediction with an Improved Online Sequential Extreme Learning Machine
The efficient data investigation for fast and accurate remaining useful life prediction of aircraft engines can be considered as a very important task for maintenance operations. In this context, the key issue is how an …
Aircraft engines Remaining Useful Life prediction with an adaptive denoising online sequential Extreme Learning Machine
Remaining Useful Life (RUL) prediction for aircraft engines based on the available run-to-failure measurements of similar systems becomes more prevalent in Prognostic Health Management (PHM) thanks to the new advanced me…
DenoisingExponential degradationManagementVariational encoding approach for interpretable assessment of remaining useful life estimation
A new method for evaluating aircraft engine monitoring data is proposed. Commonly, prognostics and health management systems use knowledge of the degradation processes of certain engine components together with professio…
ManagementRemaining Useful Lifetime EstimationVariational InferenceFederated learning framework for collaborative remaining useful life prognostics: an aircraft engine case study
Complex systems such as aircraft engines are continuously monitored by sensors. In predictive aircraft maintenance, the collected sensor measurements are used to estimate the health condition and the Remaining Useful Lif…
Federated LearningA stacked deep convolutional neural network to predict the remaining useful life of a turbofan engine
This paper presents the data-driven techniques and methodologies used to predict the remaining useful life (RUL) of a fleet of aircraft engines that can suffer failures of diverse nature. The solution presented is based …
Bayesian OptimizationModel SelectionRemaining Useful Lifetime Estimation