  In recent years, deep learning has led to significant advances in bearing
fault diagnosis (FD). Most techniques aim to achieve greater accuracy. However,
they are sensitive to noise and lack robustness, resulting in insufficient
domain adaptation and anti-noise ability. The comparison of studies reveals
that giving equal attention to all features does not differentiate their
significance. In this work, we propose a novel FD model by integrating
multi-scale quaternion convolutional neural network (MQCNN), bidirectional
gated recurrent unit (BiGRU), and cross self-attention feature fusion (CSAFF).
We have developed innovative designs in two modules, namely MQCNN and CSAFF.
Firstly, MQCNN applies quaternion convolution to multi-scale architecture for
the first time, aiming to extract the rich hidden features of the original
signal from multiple scales. Then, the extracted multi-scale information is
input into CSAFF for feature fusion, where CSAFF innovatively incorporates
cross self-attention mechanism to enhance discriminative interaction
representation within features. Finally, BiGRU captures temporal dependencies
while a softmax layer is employed for fault classification, achieving accurate
FD. To assess the efficacy of our approach, we experiment on three public
datasets (CWRU, MFPT, and Ottawa) and compare it with other excellent methods.
The results confirm its state-of-the-art, which the average accuracies can
achieve up to 99.99%, 100%, and 99.21% on CWRU, MFPT, and Ottawa datasets.
Moreover, we perform practical tests and ablation experiments to validate the
efficacy and robustness of the proposed approach. Code is available at
https://github.com/mubai011/MQCCAF.
