arXiv:2608. 08301v1 Announce Type: new Abstract: Reliable identification of railway wheel defects is important for safety and maintenance.
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.
The paper introduces a hybrid two‑stage machine learning pipeline for fault detection and classification in high‑voltage transmission networks. Stage 1 uses an Isolation Forest anomaly detector combined with an optional supervised binary detector, while Stage 2 applies a Random Forest multiclass classifier only to samples flagged by Stage 1. Feature engineering maps six raw channels to eighteen features, including zero‑sequence symmetrical components, achieving end‑to‑end accuracies of 95.8 % on the TLFaultDataset and 97.25 % on an independent single‑point dataset, surpassing federated benchmarks without GPU or federated infrastructure.
The paper introduces a fault‑diagnosis framework for electrical submersible pumps that uses Siamese contrastive representation learning to handle class imbalance and a prior‑corrected k‑nearest neighbor classifier for robust fault classification. It extracts discriminative vibration‑domain features, trains a Siamese network to cluster same‑fault samples, and applies a distance‑weighted KNN to mitigate imbalance. Validation with a Leave‑One‑ESP‑Out strategy shows consistent performance across unseen pump units, indicating potential for reliable predictive maintenance in offshore oil production.
arXiv:2606. 08247v1 Announce Type: cross Abstract: Acute asthma risk assessment requires rapid interpretation of respiratory sounds, oxygenation, airflow limitation, speech ability, work of breathing, mental status, and response to reliever therapy.
arXiv:2608.28602v1 Announce Type: new Abstract: Parkinson disease PD is a progressive neurodegenerative disease that can have a significant impact on motor performance resulting in the appearance of...
arXiv:2607. 22797v1 Announce Type: cross Abstract: Trustworthy deployment of AI-based diagnosis in safety-critical mechanical systems hinges on validation: whether a prediction can be checked against physical reality before it is acted upon.
The paper introduces an automated defect detection system for printed circuit board (PCB) pin insertion, combining U‑Net semantic segmentation with contour‑based feature extraction and logistic regression for board‑level pass/fail classification. Segmentation masks generate contour representations of individual pins, from which features such as average contour size are extracted to train the classifier. Evaluated on an industrial dataset and a public PCB pin‑inspection dataset, the method achieved ROC‑AUC scores of 0.990 and 1.000, outperforming PatchCore and instance‑segmentation baselines.