ECNN: Intelligent Fault Diagnosis Method Using Efficient Convolutional Neural Network

With outstanding deep feature learning and nonlinear classification abilities, Convolutional Neural Networks (CNN) have been gradually applied to deal with various fault diagnosis tasks. Affected by variable working conditions and strong noises, the empirical datum always has different probability distributions, and then different data segments may have inconsistent contributions, so more attention should be assigned to the informative data segments. However, most of the CNN-based fault diagnosis methods still retain black-box characteristics, especially the lack of attention mechanisms and ignoring the special contributions of informative data segments. To address these problems, we propose a new intelligent fault diagnosis method comprised of an improved CNN model named Efficient Convolutional Neural Network (ECNN). The extensive view can cover the special characteristic periods, and the small view can locate the essential feature using Pyramidal Dilated Convolution (PDC). Consequently, the receptive field of the model can be greatly enlarged to capture the location information and excavate the remarkable informative data segments. Then, a novel residual network feature calibration and fusion (ResNet-FCF) block was designed, which uses local channel interactions and residual networks based on global channel interactions for weight-redistribution. Therefore, the corresponding channel weight is increased, which puts more attention on the information data segment. The ECNN model has achieved encouraging results in information extraction and feature channel allocation of the feature. Three experiments are used to test different diagnosis methods. The ECNN model achieves the highest average accuracy of fault diagnosis. The comparison results show that ECNN has strong domain adaptation ability, high stability, and superior diagnostic performance.

Automated summary

This article proposes a new intelligent fault diagnosis method that can better handle fault diagnosis tasks under different working conditions and noise interference through an improved CNN model and a residual network feature calibration and fusion block.