  Although the deep learning recognition model has been widely used in the
condition monitoring of rotating machinery. However, it is still a challenge to
understand the correspondence between the structure and function of the model
and the diagnosis process. Therefore, this paper discusses embedding
distributed attention modules into dense connections instead of traditional
dense cascading operations. It not only decouples the influence of space and
channel on fault feature adaptive recalibration feature weights, but also forms
a fusion attention function. The proposed dense fusion focuses on the
visualization of the network diagnosis process, which increases the
interpretability of model diagnosis. How to continuously and effectively
integrate different functions to enhance the ability to extract fault features
and the ability to resist noise is answered. Centrifugal fan fault data is used
to verify this network. Experimental results show that the network has stronger
diagnostic performance than other advanced fault diagnostic models.
