Bearing Fault Diagnosis based on Feature Visualization and Depth Adaptive Network - Hong Jiang; Yu Feng; Rong Fu (Jixie chuandong)
Aiming at the problem that feature extraction in bearing fault diagnosis needs to rely heavily on manual experience and expert knowledge，a bearing fault diagnosis method based on Gramian angle field（GAF） transformation and adaptive depth network is proposed. Firstly，the collected signals are analyzed by empirical mode decomposition method， and the Intrinsic mode functions（IMFs） are effectively determined by Mahalanobis distance measurement method. It can not only select the similar modal components of the noisy signal and the original signal to improve the signal-to-noise ratio of the noisy signal，but also eliminate the similar modal components of different types of signals to highlight the signal characteristics； then，the signal is reconstructed by using the selected IMFs，and the reconstructed signal is visualized based on GAF transform； finally，the depth adaptive network is used for feature learning and state recognition. The results show that the accuracy of the proposed method is 94.97%，which is better than the common vibration signal fault diagnosis methods，and the proposed method can also suppress the noise and has good robustness，which provides a reasonable idea for the intelligent and accurate diagnosis of bearings.