Bearing fault identification based on convolutional neural network by different input modes

Abstract

Convolutional neural networks (CNNs) have been applied to the field of fault diagnosis as one of the most widely used deep learning architectures. Different input modes of CNN for bearing fault identification were analyzed by researchers to improve recognition accuracy, such as time-domain diagram, grayscale diagram, short-time Fourier transform diagram, and continuous wavelet transform diagram. However, for the data with small sample size and high background noise, the performance of the CNN is constrained. In this paper, one CNN input mode for bearing fault recognition is proposed based on time-domain color feature diagram (TDCF) through adding red color to diagrams. The method significantly enhanced the fault characteristics of the signal, which is beneficial to the CNN extraction of bearing fault features. Convolution visualization illustrates the effectiveness of the proposed method that provides more bearing fault recognition information. Different sample size and color rate were analyzed by bearing vibration data with high noise. The results showed that the bearing fault identification method based on CNN with 0.4 TDCF obtained a highest fault identification accuracy compared with other input mode methods. The feasibility of the proposed method has been validated, which also provides one reference for other faults identification and pattern recognition.