Deep morphological convolutional network for feature learning of vibration signals and its applications to gearbox fault diagnosis

Vibration signals are utilized widely for machinery fault diagnosis. These typical deep neural networks (DNNs), e.g., convolutional neural networks (CNNs) perform well in feature learning of vibration signals for machinery fault diagnosis. However, vibration signals collected from real industry are often noised and nonstationary. It is still a challenging problem for CNNs to implement feature extraction and noise reduction from a large number of vibration signals. In this paper, a novel DNN, deep morphological convolutional network (DMCNet) is proposed for feature learning of gearbox vibration signals. Firstly, two parallel branches with different morphological operations (i.e., opening and closing) are adopted to filter out the noise and perform feature mapping on vibration signals. The structure elements of morphological operation can be updated in the network training by using the back-propagation learning algorithm. Secondly, a kurtosis-based feature fusion method is proposed to enhance channels with strong impulsive features. Finally, a recalibrated residual learning is further proposed for feature learning on the morphological feature maps, where a recalibrated skip connection is used for feature selection. The experimental results indicate that DMCNet can implement feature leaning of gearbox vibration signals well and outperform those state-of-the-art CNNs.