arXiv Machine Learning June 19, 2026 By Reza Jalayer, Masoud Jalayer, Andrea Mor, Carlotta Orsenigo, Carlo Vercellis

Evaluating deep learning models for fault diagnosis of a rotating machinery with epistemic and aleatoric uncertainty

Read the original on arXiv Machine Learning →

arXiv:2412. 18980v2 Announce Type: replace Abstract: Uncertainty-aware deep learning (DL) models recently gained attention in fault diagnosis as a way to promote the reliable detection of faults when out-of-distribution (OOD) data arise from unseen faults (epistemic uncertainty) or the presence of noise (aleatoric uncertainty).

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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