Deep ensemble models, particularly a heterogeneous four-member ensemble, outperform MC-dropout and Bayesian neural networks at flagging out-of-distribution fault data in bearing diagnosis, under both epistemic and aleatoric uncertainty.
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Evaluating deep learning models for fault diagnosis of a rotating machinery with epistemic and aleatoric uncertainty
Deep ensemble models, particularly a heterogeneous four-member ensemble, outperform MC-dropout and Bayesian neural networks at flagging out-of-distribution fault data in bearing diagnosis, under both epistemic and aleatoric uncertainty.