CNN-LSTM model predicts nine functional variables with uncertainty estimates for an angle grinder and integrates finite-element fatigue analysis to produce reliability trajectories for reuse decisions.
Yuling Zhan, Ziqian Kong, Ziqi Wang, Xiaohang Jin, and Zhengguo Xu
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2026 2verdicts
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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.
citing papers explorer
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Uncertainty Aware Functional Behavior Prediction and Material Fatigue Assessment for Circular Factory
CNN-LSTM model predicts nine functional variables with uncertainty estimates for an angle grinder and integrates finite-element fatigue analysis to produce reliability trajectories for reuse decisions.
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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.