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 a...
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| Veröffentlicht in: | Applied soft computing Jg. 88; S. 106060 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Elsevier B.V
01.03.2020
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| ISSN: | 1568-4946, 1872-9681 |
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| Abstract | 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.
•A new sPSDAE model is proposed to diagnose bearing faults.•Proposed fully connected network enhances information transfer efficiency significantly.•Pruning operation is designed to reduce the network redundancy.•Proposed feature fusion method shares feature information and guarantees the uniqueness of the feature dimensions.•A variety of methods are used to optimize the sparsity of the model. |
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| AbstractList | 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.
•A new sPSDAE model is proposed to diagnose bearing faults.•Proposed fully connected network enhances information transfer efficiency significantly.•Pruning operation is designed to reduce the network redundancy.•Proposed feature fusion method shares feature information and guarantees the uniqueness of the feature dimensions.•A variety of methods are used to optimize the sparsity of the model. |
| ArticleNumber | 106060 |
| Author | Wu, Jun Shao, Xinyu Zhang, Cong Cheng, Jiaxin Zhu, Haiping |
| Author_xml | – sequence: 1 givenname: Haiping orcidid: 0000-0002-5989-012X surname: Zhu fullname: Zhu, Haiping organization: School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China – sequence: 2 givenname: Jiaxin surname: Cheng fullname: Cheng, Jiaxin organization: School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China – sequence: 3 givenname: Cong surname: Zhang fullname: Zhang, Cong organization: School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China – sequence: 4 givenname: Jun orcidid: 0000-0002-8657-5475 surname: Wu fullname: Wu, Jun email: wuj@hust.edu.cn organization: School of Naval Architecture and Ocean Engineering, Huazhong University of Science and Technology, Wuhan, China – sequence: 5 givenname: Xinyu surname: Shao fullname: Shao, Xinyu organization: School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China |
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| Keywords | Deep learning Fault diagnosis Stacked pruning sparse denoising autoencoder Pruning operation Rolling bearing |
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| SubjectTerms | Deep learning Fault diagnosis Pruning operation Rolling bearing Stacked pruning sparse denoising autoencoder |
| Title | Stacked pruning sparse denoising autoencoder based intelligent fault diagnosis of rolling bearings |
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