A Novel Attentional Feature Fusion with Inception Based on Capsule Network and Application to the Fault Diagnosis of Bearing with Small Data Samples

Abstract

Fault diagnosis of bearing with small data samples is always a research hotspot in the field of bearing fault diagnosis. To solve the problem, a convolutional block attention module (CBAM)-based attentional feature fusion with an inception module based on a capsule network (Capsnet) is proposed in the paper. Firstly, the original vibration signal is decomposed into multiple intrinsic mode function (IMF) sub-signals by the ensemble empirical mode decomposition (EEMD), and then the original vibration signal and the corresponding former four order IMF sub-signals are input into the inception modules to extract the features. Secondly, these features are concatenated and optimized by the CBAM. Finally, the selected sensitive features are fed into the Capsnet to diagnose the faults. Through the multifaceted experiment analysis on fault diagnosis of bearing with small data samples, the diagnosis results demonstrate that the proposed attentional feature fusion with inception based on Capsnet not only diagnoses the fault of bearing with small data samples, but also is superior to other feature fusion methods, such as feature fusion with inception based on Capsnet and attentional feature fusion with inception based on CNN, etc., and other single diagnosis models such as Capsnet with CBAM and inception, and CNN with CBAM and inception.