The paper evaluates SelF‑Rocket, a random convolutional kernel-based method, for multi‑class diagnosis of mechanical and electrical faults in rotating machinery. It introduces a multivariate extension of SelF‑Rocket and compares it with leading ROCKET‑based methods on two public benchmarks, MaFaulDa and ITSC‑UDG. Results show SelF‑Rocket achieves the best accuracy‑latency trade‑off, excelling on MaFaulDa and remaining competitive on the more challenging ITSC‑UDG dataset.
arXiv:2509. 22267v5 Announce Type: replace Abstract: Reliable detection of bearing faults is essential for maintaining the safety and operational efficiency of rotating machinery.
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: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: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:2609.14180v1 Announce Type: new Abstract: Fault detection in autonomous VTOL aircraft is critical because even minor component degradations can rapidly destabilize multirotor vehicles operating...
arXiv:2606. 24173v1 Announce Type: cross Abstract: On-device fault detection enables real-time diagnostics without cloud dependency, but deploying machine learning models on resource-constrained hardware demands careful tradeoffs between accuracy, latency, and model size.
arXiv:2609.07493v1 Announce Type: new Abstract: In this paper, we propose a class-wise dimension (channel) selection framework for Multivariate Time Series Classification (MTSC). Rather than applying...
arXiv:2510. 00831v2 Announce Type: replace Abstract: The increasing complexity of modern power systems, driven by the integration of inverter-based and distributed energy resources, challenges the reliability of conventional protection schemes and motivates the use of machine learning for protection tasks.
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:2607. 16493v1 Announce Type: new Abstract: Multi-task deep learning models that jointly perform fault classification and remaining useful life (RUL) regression are increasingly used in predictive maintenance, yet reported performance can be strongly affected by how sliding-window sequences are split into training and test sets.
The paper introduces DualRes, a compact oscillatory state‑space model designed for vibration‑based fault diagnosis when labeled data are scarce and computational resources are limited. DualRes integrates two spectral views of vibration and employs selective oscillatory memory to learn how long to retain temporal patterns, resulting in a lightweight encoder with only 39,528 parameters. Evaluations on six bearing datasets and a gearbox benchmark show that DualRes outperforms nine competing methods across most label budgets, achieving significant gains in macro‑F1, faster inference, and reduced storage requirements.