A High-Stability Diagnosis Model Based on a Multiscale Feature Fusion Convolutional Neural Network

Abstract

Recently, the diagnosis of rotating machines based on deep learning models has achieved great success. Many of these intelligent diagnosis models are assumed that training and test data are subject to independent identical distributions (IIDs). Unfortunately, such an assumption is generally invalid in practical applications due to noise disturbances and changes in workload. To address the above problem, this article presents a high-stability diagnosis model named the multiscale feature fusion convolutional neural network (MFF-CNN). MFF-CNN does not rely on tedious data preprocessing and target domain information. It is composed of multiscale dilated convolution, self-adaptive weighting, and the new form of maxout (NFM) activation. It extracts, modulates, and fuses the input samples' multiscale features so that the model focuses more on the health state difference rather than the noise disturbance and workload difference. Two diagnostic cases, including noisy cases and variable load cases, are used to verify the effectiveness of the present model. The results show that the present model has a strong health state identification capability and anti-interference capability for variable loads and noise disturbances.