A modified transformer based on adaptive frequency enhanced attention, large kernel convolution, and multiscale implementation for bearing fault diagnosis.

ABSTRACT: Bearing fault diagnosis has attracted increasing attention due to its critical role in monitoring the health of rotating machinery. Data-driven models based on deep learning (DL) have demonstrated strong capabilities in feature extraction. However, their performance often degrades under strong noise interference, which limits their applicability in real-world industrial scenarios. To address this issue, this paper proposes a novel attention-enhanced Transformer model that integrates large-kernel convolution and multiscale CNN structures for robust fault diagnosis. The proposed framework effectively combines spatiotemporal feature modeling with adaptive frequency-domain enhancement, enabling it to suppress noise and highlight informative diagnostic features. Experimental results on the Pade

SUBMITTER: Chang X 

PROVIDER: S-EPMC12464161 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature