Bearing Fault Diagnosis Based on Multiscale Lightweight Convolutional Neural Network

Abstract

Many bearing fault diagnosis methods often struggle to balance between adequate feature extraction and lightweight property, which makes it somewhat difficult to fulfill the accuracy and efficiency required for practical applications. To address this issue, this study describes the development of a multiscale lightweight deep learning model for accurate bearing fault diagnosis. Specifically, the Gaussian pyramid method, which can create a series of images at different scales, is employed to express the Gramian angular field (GAF) matrix images generated by transforming the bearing vibration signals to avoid the common problem of insufficient feature extraction of a single-scale image. At the same time, the dependencies between feature channels are extracted using a lightweight attention mechanism utilized in deep learning, known as Efficient Channel Attention (ECA), to improve the capability of feature representation. This approach effectively improves the learning ability of bearing fault characteristics and greatly increases the accuracy of fault diagnosis. Considering the problem related to the lightweight level of the method, a Ghost module, a type of convolution neural network system, is also employed to generate more features by using fewer parameters, thereby improving the overall calculation efficiency. Here we have developed a residual module based on the Ghost module and ECA, which can be easily integrated into most bearing fault diagnosis backbone networks. Based on our experimental tests, the developed system can clearly achieve high accuracy precision of bearing fault diagnosis to fulfill the needs of practical engineering while maintaining light weight. Specifically, the test accuracy of the proposed method using two bearing fault datasets exceeds 99.4%, and the giga floating-point operations (GFLOPs) is only 1.99, which can fully meet the needs of practical engineering.