Structured-effect neural network with variational Bayesian inference predicts remaining useful life while claiming both accuracy and interpretability, evaluated on aircraft engine failure data.
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cs.LG 2years
2019 2verdicts
UNVERDICTED 2representative citing papers
A domain-adversarial LSTM learns invariant features from labeled source RUL data to enable predictions in unlabeled target domains with distribution shifts due to varying conditions and fault modes.
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Forecasting remaining useful life: Interpretable deep learning approach via variational Bayesian inferences
Structured-effect neural network with variational Bayesian inference predicts remaining useful life while claiming both accuracy and interpretability, evaluated on aircraft engine failure data.
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Remaining Useful Lifetime Prediction via Deep Domain Adaptation
A domain-adversarial LSTM learns invariant features from labeled source RUL data to enable predictions in unlabeled target domains with distribution shifts due to varying conditions and fault modes.