Bearing Fault Diagnosis Based on Natural Adaptive Moment Estimation Algorithm and Improved Octave Convolution

Abstract

Fault diagnosis of rolling bearing has been the focus of research. Bearing signals are often accompanied by similar information, resulting in redundancy between data. Moreover, rolling bearing is often used in situations with large background noise, so extracting the characteristic value of rolling bearing signal and removing noise from the signal are of great significance. This paper presents a fault diagnosis model combining NAdam(Natural Adaptive Moment Estimation) algorithm and improved octave convolution. First, natural exponential decay function is proposed to replace the exponential decay function for parameter updating of Adam(Adaptive Moment Estimation). Compared with the exponential decay function, the natural exponential decay function can accelerate the convergence rate of the model. The internal structure in octave convolution is then improved. The improved structure can improve feature extraction and eliminate data redundancy. Finally, the dilated gate convolution layer is used to filter and classify the data. According to the simulation test of the case western reserve university data set and laboratory power equipment data set, the accuracy rate can reach more than 98%. Experiments with variable load and signal noise ratio are carried out to verify the noise resistance and generalization performance of the proposed method.