Deep Morphological Shrinkage Convolutional Autoencoder-Based Feature Learning of Vibration Signals for Gearbox Fault Diagnosis

Fault diagnosis is significant to guarantee the safety and reliability of the machinery. Local fault will make the collected vibration signals deviate from the normal signals. In order to extract fault-related features from vibration signals, many deep learning-based methods have been proposed for machinery fault diagnosis recently. However, due to the extreme conditions (e.g., high background noise, limited labeled training samples), it is a challenging task to implement features extraction from the collected signals with non-linear and non-stationary characteristics. To implement feature extraction and noise reduction of vibration signals, this paper proposes a novel network, i.e., deep morphological shrinkage convolutional auto-encoder (DMSCAE) for gearbox fault diagnosis considering the insufficient labeled training samples. Firstly, a morphological convolutional auto-encoder is proposed for noise filtering and feature extraction. Secondly, a multi-branch structure with different structural elements (SEs) is used in the morphological layer to extract impulsive components. Finally, a soft thresholding-based shrinkage is employed to filter ineffective features, where an adaptative method is developed to adjust the threshold automatically in the back-propagation procedure. The experiments on two gearbox fault diagnosis tests are conducted to verify the performance of DMSCAE. The results indicate that DMSCAE obtains a better performance for fault diagnosis than other DNNs, e.g., efficient channel attention network (ECANet), self-calibrated convolutional network (SCNet).