MATE-ViT: A multi-channel contrast-limited adaptive time-frequency enhancement and vision transformer framework for bearing fault diagnosis

To address the issues of low contrast, unclear local details, and the difficulty of existing deep learning models in capturing long-range dependencies in time-frequency images generated by current time-frequency construction methods, this paper proposes a bearing fault diagnosis framework (MATE-ViT) based on multi-channel contrast-limited adaptive time-frequency enhancement (CLAHE) and vision transformer (ViT). First, an improved CLAHE algorithm is used to independently enhance the multi-channel time-frequency images, effectively improving the local contrast and detail expression of the images, thereby enhancing the recognizability of fault features. Next, a perception-driven image segmentation and rearrangement strategy is applied, dividing the enhanced image into local feature patches and rearranging them to maximize the complementarity between local features and global information, thus providing optimized input feature representations for subsequent global modeling by the transformer. Finally, the ViT model is applied to extract global features from the enhanced images, achieving accurate classification of bearing fault types. Experimental results show that the proposed MATE-ViT framework outperforms mainstream deep learning models in terms of diagnostic accuracy and robustness under various noise levels and operating conditions, offering a more effective and reliable approach for bearing fault diagnosis.