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.
By Victor Gialis, Maxime Metz, David Esteve, Abdenour Soualhi
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.
By Jinghan Wang, Feng Cheng, Wentao Wu, Hang Li, Gaoliang Peng, Tianchen Liu
The paper presents a factorized-axis convolutional gated recurrent unit (GRU) with dynamic adaptive pooling (DAP) for predicting the remaining useful life (RUL) of rolling bearings from time‑frequency representations (TFRs). It introduces multiscale anisotropic convolution, a dual‑axis convolution block attention module, and Monte Carlo dropout for uncertainty estimation, addressing the directional structure challenges in TFRs. Experiments on two public bearing datasets show that this approach outperforms existing RUL prediction methods and that the factorized axis design and adaptive pooling contribute to lower mean errors.
By Hanbyeol Park, Jungho Choo, Hyerim Bae
arXiv:2509. 22267v5 Announce Type: replace Abstract: Reliable detection of bearing faults is essential for maintaining the safety and operational efficiency of rotating machinery.
By Jo\~ao Paulo Vieira, Victor Afonso Bauler, Rodrigo Kobashikawa Rosa, Danilo Silva
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.
By Yanxi Ding, Tingyue Jia
Convolutional neural networks (CNN) are widely used to predict the remaining useful life (RUL) of rolling bearings from time-frequency representations (TFRs) of vibration signals. However, during degr...