IInception-CBAM-IBiGRU based fault diagnosis method for asynchronous motors.

ABSTRACT: Aiming at the problems of insufficient extraction of asynchronous motor fault features by traditional deep learning algorithms and poor diagnosis of asynchronous motor faults in robust noise environments, this paper proposes an end-to-end fault diagnosis method for asynchronous motors based on IInception-CBAM-IBiGRU. The method first uses a signal-to-grayscale image conversion method to convert one-dimensional vibration signals into two-dimensional images and initially extracts shallow features through two-dimensional convolution; then the Improved Inception (IInception) module is used as a residual block to learning features at different scales with a residual structure, and extracts its important feature information through the Convolutional Block Attention Module (CBAM) to extract impor

SUBMITTER: Li Z 

PROVIDER: S-EPMC10908808 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature