Deep learning networks are considered as end-to-end black-box models. Performing interpretable analysis of these networks can facilitate a deeper understanding of their internal operations, enabling rational optimization of network architecture and parameter tuning. Currently, bearing fault diagnosis models based on Transformer networks require the use of methods such as time-frequency analysis to elevate the dimensionality of time-domain signals, transforming them into two-dimensional images like time-frequency graphs for interpretable analysis. However, this approach has drawbacks, including fixed parameters during the dimensionality expansion process, excessive network parameterization, and limited interpretability. To address these issues, we propose a Convolutional Self-Attention Adaptive Dimension-Increasing Network (CSADI-Net) that combines the integration of an in-network adaptive dimensionality expansion method, a Convolutional Self-Attention Module, and Mid Layer Class Activation Maps (ML-CAM). The Convolutional Self-Attention Module efficiently computes query (Q), key (K), and value (V) matrices from feature maps using convolutional layers, significantly reducing the number of trainable parameters. The in-network adaptive dimensionality expansion method seamlessly incorporates the dimensionality expansion process with network training, endowing it with adaptive parameter tuning capabilities. ML-CAM visualizes the network's focus on various regions of the two-dimensional feature maps obtained through the in-network dimensionality expansion method, offering an intuitive and visually interpretable analytical approach. Furthermore, we conducted tests on CSADI-Net and ML-CAM. CSADI-Net achieved an accuracy rate of 97.32±0.12% on the Case Western Reserve University bearing dataset and achieved complete classification on the experimentally measured bearing fault dataset from Dalian University. Additionally, ML-CAM was employed to generate class activation heatmaps for each sample in both datasets, elucidating the network's operational mechanisms. These results affirm that CSADI-Net possesses merits such as high accuracy, robust noise resistance, and strong interpretability.