Fault Diagnosis of Roller Bearings Based on a Wavelet Neural Network and Manifold Learning

Abstract

In order to improve the accuracy of the fault diagnosis of roller bearings, this paper proposes a kind of fault diagnosis algorithm based on manifold learning combined with a wavelet neural network. First, a high-dimensional feature signal set is obtained using a conventional feature extraction algorithm; second, an improved Laplacian characteristic mapping algorithm is proposed to reduce the dimensions of the characteristics and obtain an effective characteristic signal. Finally, the processed characteristic signal is inputted into the constructed wavelet neural network whose output is the types of fault. In the actual experiment of recognizing data sets on roller bearing failures, the validity and accuracy of the method for diagnosing faults was verified.