Deep sparse representation network for feature learning of vibration signals and its application in gearbox fault diagnosis

摘要

Vibration signals play a key role in machinery fault diagnosis, which are often buried by strong noises due to complex working conditions. Typical deep neural networks (e.g., convolutional neural networks (CNNs)) can effectively learn features from vibration data and perform well in machinery fault diagnosis. Nonetheless, the vibration signals collected from real industry are often noised and nonstationary. It is still difficult for CNNs to implement feature extraction and noise reduction from large number of vibration signals. In this paper, a novel deep learning model, deep sparse representation network (DSRNet) is proposed to suppress noise and learn effective features from noised signals directly. Firstly, a sparse representation layer is developed to extract impulsive components and suppress noise hidden in vibration signals in an end-to-end manner. Secondly, an adaptive densely stacked convolutional structure is proposed to extract effective features from the filtered signals by the sparse representation layer. Finally, the effectiveness of DSRNet for feature learning on vibration signals is verified on two gearbox cases. The experimental results show that DSRNet has good feature learning and signal denoising performance, which outperforms those famous CNNs (e.g., ResNet, DenseNet).