In order to solve the problem of low accuracy of damage size identification of rolling bearing based on machine learning

a method of fault damage size identification of rolling bearing based on deep residual network is proposed. This method takes residual network as the main frame of deep feature extraction

and establishes a network model that can map the preprocessed vibration sample data to the corresponding damage size. Fourier transform is used in data preprocessing to obtain the spectrum of the original vibration acceleration time-domain signal as the input of the network. The proposed damage size identification method is verified by different damage size tests on two types of testers such as rolling bearing accelerated fatigue tester and aero-engine rotor tester

and compared with other methods. The results show that

within the prediction error range of 0.3 mm

the recognition accuracy of the network model for the fault damage size without training is 91.2% for the inner ring and 97.9% for the outer ring. At the same time

the prediction error of the damage size can still reach the recognition accuracy within 0.3 mm after the data is processed with noise. The results fully show that this method has a strong ability to identify fault damage size.