Construction of the efficient attention prototypical net based on the time–frequency characterization of vibration signals under noisy small sample

Abstract

The deep learning net has achieved breakthrough results in fault diagnosis. However, most deep learning algorithms rely on massive volumes of data and are difficult to resist the influence of noise. In this study, a new method based on the efficient attention prototypical net (EAPN) is proposed to address the problems. First, frequency slice wavelet transform (FSWT) that can make the signal well described is introduced to convert the signal into images. Subsequently, the efficient lightweight channel attention (ELCA) module and dilated convolution module are created to extract key information across channels under the greater receptive field. Finally, the improved prototypical network is applied to classify based on metric space. Compared with other methods, this paper verified the higher performance of the EAPN under limited samples and strong noise based on the standard bearing fault data set and the actual collected rotor fault data.