Bearing Fault Diagnosis Based on the Markov Transition Field and SE-IShufflenetV2 Model

A bearing fault diagnosis method based on the Markov transitionfield(MTF)and SEnet(SE)-IShufflenetV2 model is proposed in this paper due to the problems of complex working conditions,low fault diagnosis accuracy,and poor generalization of rolling bearing.Firstly,MTF is used to encode one-dimensional time series vibration sig-nals and convert them into time-dependent and unique two-dimensional feature images.Then,the generated two-dimensional dataset is fed into the SE-IShufflenetV2 model for training to achieve fault feature extraction and classification.This paper selects the bearing fault datasets from Case Western Reserve University and Paderborn University to experimentally verify the effectiveness and superiority of the proposed method.The generalization performance of the proposed method is tested under the variable load condition and different signal-to-noise ratios(SNRs).The experimental results show that the average accuracy of the proposed method under different working conditions is 99.2%without adding noise.The accuracy under different working conditions from 0 to 1 HP is 100%.When the SNR is 0 dB,the average accuracy of the proposed method can still reach 98.7%under varying working conditions.Therefore,the bearing fault diagnosis method proposed in this paper is characterized by high accuracy,strong anti-noise ability,and generalization.Moreover,the proposed method can also overcome the influence of variable working conditions on diagnosis accuracy,providing method support for the accurate diagnosis of bearing faults under strong noise and variable working conditions.