Stacked pruning sparse denoising autoencoder based intelligent fault diagnosis of rolling bearings

This paper proposes a new stacked pruning sparse denoising autoencoder (sPSDAE) model for intelligent fault diagnosis of rolling bearings. Different from the traditional autoencoder, the proposed sPSDAE model, including a fully connected autoencoder network, uses the superior features extracted in all the previous layers to participate in the subsequent layers. This means that some new channels are created to connect the front layers and the back layers, which reduces information loss. To improve the training efficiency and precision of the sPSDAE model, a pruning operation is added into the sPSDAE model so as to prohibit non-superior units from participating in all the subsequent layers. Meanwhile, a feature fusion mechanism is introduced to ensure the uniqueness of the feature dimensions. After that, the sparse expression of the sPSDAE model is strengthened, thereby improving the generalization ability. The proposed method is evaluated by using a public bearing dataset and is compared with other popular fault diagnosis models. The results show that the ability of the sPSDAE model to extract features is significantly enhanced and the phenomenon of gradient disappearance is further reduced. The proposed model achieves higher diagnostic accuracy than other popular fault diagnosis models. (C) 2020 Elsevier B.V. All rights reserved.