Traditional fault diagnosis methods for rolling bearings are difficult to accurately and efficiently achieve fault classification.A method of rolling bearing fault classification based on symmetrized dot pattern(SDP)and improved SAM⁃MobileNetv2 was proposed.Firstly

the bearing vibration signal was transformed into two⁃dimensional images with rich characteristic information by SDP algorithm.Secondly

the two⁃dimensional images were fed into the SAM⁃MobileNetv2 network model

which extracted and classified fault feature information.Improved SAM⁃MobileNetv2 networks used the adaptive activation function ACON to replace the ReLU6 activation function in SAM⁃MobileNetv2 to improve model classification performance.Finally

this model was compared with various models.The experimental results show that this model can accurately and efficiently realize the classification of rolling bearing faults

using Case Western Reserve University bearing fault data with an accuracy rate of 99.5%

using the University of Ottawa bearing failure data with an accuracy rate of 97.2%.