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: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.
arXiv:2606. 24459v1 Announce Type: new Abstract: Bearing fault diagnosis faces critical challenges when dataset heterogeneity, operating condition variations, and limited labeled data occur simultaneously in industrial environments.
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. 09174v1 Announce Type: new Abstract: To address the degradation of bearing fault diagnosis accuracy under strong noise, this paper proposes a time-frequency dual-domain multi-scale convolutional neural network.
arXiv:2509. 03070v5 Announce Type: replace-cross Abstract: This letter presents a CWT-enhanced vibration sensing framework for bearing fault monitoring through localized time-frequency region detection on continuous wavelet transform (CWT) spectrograms.
arXiv:2512. 24679v2 Announce Type: replace Abstract: Intelligent fault diagnosis has become an indispensable technique for ensuring machinery reliability.
Vibration-based health monitoring of rotating machinery requires reliable fault diagnosis under operational data constraints, yet condition assessment remains challenged by structural scarcity of fault events and heterogeneous sim-to-real gaps in digital twin-generated signals. Each fault type generates impulses with distinct periodicity, amplitude modulation, and spectral character, making feature-space discrepancies fundamentally heterogeneous across fault classes.
arXiv:2606. 24954v1 Announce Type: new Abstract: Vibration-based health monitoring of rotating machinery requires reliable fault diagnosis under operational data constraints, yet condition assessment remains challenged by structural scarcity of fault events and heterogeneous sim-to-real gaps in digital twin-generated signals.
arXiv:2608. 08301v1 Announce Type: new Abstract: Reliable identification of railway wheel defects is important for safety and maintenance.
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.
arXiv:2608. 08207v1 Announce Type: cross Abstract: Multivariate Time Series Classification (MTSC) demands models that can effectively capture complex temporal patterns across multiple scales while remaining computationally efficient.
arXiv:2511. 01592v2 Announce Type: replace Abstract: Energy estimation is critical to impact identification on aerospace composites, where low-velocity impacts can induce internal damage that is undetectable at the surface.