Ball screw fault diagnosis based on continuous wavelet transform and two-dimensional convolution neural network

Abstract

Due to extreme operating conditions such as high-speed and heavy loads, ball screws are prone to damages, that affect the accuracy and operational safety of the mechanical equipment. As strong background noise and weak fault characteristics, it is difficult to capture the inherent fault state only depending on the time-domain or frequency-domain information from the vibration signal. In this paper, a fault diagnosis method for the ball screw based on continuous wavelet transform (CWT) and two-dimensional convolutional neural network (2DCNN) is proposed. The noise-reducing vibration signal is obtained via CWT. The time-frequency graph of the noise reduction signal can more comprehensively reflect the fault information of the ball screw. The time-frequency graph is used as the input to train and test the 2DCNN. Finally, diagnosis results of different types of faults reveal that the proposed CWT-2DCNN fault diagnosis method can achieve an average recognition rate of 99.67%. Compared with one-dimensional convolutional neural network (1DCNN) and traditional BP neural network, the proposed method has fast network convergence and high recognition accuracy. Time-frequency graphs of the noise-reduced signal used as fault features for classification can effectively avoid the problem of uncertainty due to the manual extraction of features. The proposed method has high application potential in the field of ball screw pair fault diagnosis.