arXiv:2608. 08301v1 Announce Type: new Abstract: Reliable identification of railway wheel defects is important for safety and maintenance.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
arXiv:2606. 26317v1 Announce Type: cross Abstract: Accurate fault diagnosis of rolling element bearings in rotating machinery is considered essential for ensuring industrial safety and enabling predictive maintenance.
arXiv:2609.22172v1 Announce Type: cross Abstract: This paper presents two interpretable machine-learning frameworks for quality screening of e-transaxle assemblies in electric vehicles: a Stagewise W...
arXiv:2509. 22267v5 Announce Type: replace Abstract: Reliable detection of bearing faults is essential for maintaining the safety and operational efficiency of rotating machinery.
arXiv:2608. 05705v1 Announce Type: cross Abstract: Deep learning is a new way for machinery fault diagnosis but requires extensive labeled data, a scarce resource in industrial settings.
The paper proposes a Diagnostic Evidence Network (DENet) that augments traditional fault‑diagnosis outputs with structured evidence, including classification, predicted characteristic frequency, and temporal localization of impulses. This evidence aligns with theoretical bearing physics and can be validated at inference time, achieving high AUROC for misclassification detection without sacrificing accuracy. A QLoRA‑adapted language model then translates DENet’s evidence into maintenance reports, markedly reducing unsupported claims.
The paper presents an integrated framework for processing and classifying invasive bird species vocalizations in noisy natural soundscapes. It uses Bayesian wavelet shrinkage with an Epanechnikov kernel prior to denoise signals, then extracts features such as MFCCs and spectral indices. Supervised models—including Random Forest, Multinomial Logistic Regression, and SVM—are evaluated, with the SVM achieving the highest accuracy (0.9398) on a 10‑dimensional MFCC set.