A hybrid framework bifurcates RUL prediction for turbofan engines into healthy and degraded regimes via LSTM autoencoder state classification, using Weibull survival analysis and probabilistic neural networks with MC dropout for uncertainty-aware estimates on the C-MAPSS dataset.
Remaining useful life estimation in prognostics using deep convolution neural networks,
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Multi-task framework with shared convolutional-LSTM encoder predicts TGTU, DTGT, and RUL plus empirical prediction intervals for turbine engine health management.
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Bifurcated Remaining Useful Life Prediction: A Hybrid Approach for Realistic Uncertainty Characterization
A hybrid framework bifurcates RUL prediction for turbofan engines into healthy and degraded regimes via LSTM autoencoder state classification, using Weibull survival analysis and probabilistic neural networks with MC dropout for uncertainty-aware estimates on the C-MAPSS dataset.
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Scientific Machine Learning for Engine Health Management and Remaining Useful Life Prediction
Multi-task framework with shared convolutional-LSTM encoder predicts TGTU, DTGT, and RUL plus empirical prediction intervals for turbine engine health management.