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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.
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. 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.
The paper introduces FreqCondNorm, a Transformer-based architecture that adds a frequency-conditioned normalization layer to unify heterogeneous time-series data for predictive maintenance. The model is pretrained on five public datasets using masked auto‑encoding and contrastive learning, achieving 99.2% accuracy on CWRU and 82.1% zero‑shot accuracy on MFPT, showing strong transfer across sampling frequencies. However, it does not improve remaining useful life prediction, indicating a mismatch between pretraining and RUL objectives that requires further study.
arXiv:2607. 29621v1 Announce Type: cross Abstract: Convolutional neural networks (CNNs) are widely used for time-series classification, but their deployment in critical domains requires understanding the temporal and spectral patterns that drive their predictions.
arXiv:2608. 07681v1 Announce Type: new Abstract: Accurate and robust time series forecasting is essential in many applications involving physical processes, such as manufacturing monitoring and astrophysical event detection.