Deep Learning Network Based on Improved Sparrow Search Algorithm Optimization for Rolling Bearing Fault Diagnosis
In recent years, deep learning has been increasingly used in fault diagnosis of rotating machinery. However, the actual acquisition of rolling bearing fault signals often contains ambient noise, making it difficult to determine the optimal values of the parameters. In this paper, a sparrow search al...
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| Published in: | Mathematics (Basel) Vol. 11; no. 22; p. 4634 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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01.11.2023
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| ISSN: | 2227-7390, 2227-7390 |
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| Abstract | In recent years, deep learning has been increasingly used in fault diagnosis of rotating machinery. However, the actual acquisition of rolling bearing fault signals often contains ambient noise, making it difficult to determine the optimal values of the parameters. In this paper, a sparrow search algorithm (LSSA) based on backward learning of lens imaging and Gaussian Cauchy variation is proposed. The lens imaging reverse learning strategy enhances the traversal capability of the algorithm and allows for a better balance of algorithm exploration and development. Then, the performance of the proposed LSSA was tested on the benchmark function. Finally, LSSA is used to find the optimal modal component K and the optimal penalty factor α in VMD-GRU, which in turn realizes the fault diagnosis of rolling bearings. The experimental results show that the model can achieve a 96.61% accuracy in rolling bearing fault diagnosis, which proves the effectiveness of the method. |
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| AbstractList | In recent years, deep learning has been increasingly used in fault diagnosis of rotating machinery. However, the actual acquisition of rolling bearing fault signals often contains ambient noise, making it difficult to determine the optimal values of the parameters. In this paper, a sparrow search algorithm (LSSA) based on backward learning of lens imaging and Gaussian Cauchy variation is proposed. The lens imaging reverse learning strategy enhances the traversal capability of the algorithm and allows for a better balance of algorithm exploration and development. Then, the performance of the proposed LSSA was tested on the benchmark function. Finally, LSSA is used to find the optimal modal component K and the optimal penalty factor α in VMD-GRU, which in turn realizes the fault diagnosis of rolling bearings. The experimental results show that the model can achieve a 96.61% accuracy in rolling bearing fault diagnosis, which proves the effectiveness of the method. |
| Audience | Academic |
| Author | Yue, Xiaofeng Liu, Zeyuan Ma, Guoyuan Zhu, Juan Lu, Shibo |
| Author_xml | – sequence: 1 givenname: Guoyuan surname: Ma fullname: Ma, Guoyuan – sequence: 2 givenname: Xiaofeng surname: Yue fullname: Yue, Xiaofeng – sequence: 3 givenname: Juan surname: Zhu fullname: Zhu, Juan – sequence: 4 givenname: Zeyuan surname: Liu fullname: Liu, Zeyuan – sequence: 5 givenname: Shibo surname: Lu fullname: Lu, Shibo |
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| SubjectTerms | Accuracy Algorithms Bearings Deep learning fault detection Fault diagnosis Food Lagrange multiplier Lenses Methods Neural networks Roller bearings Rotating machinery Search algorithms Signal processing sparrow search algorithm Spectrum analysis |
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| Title | Deep Learning Network Based on Improved Sparrow Search Algorithm Optimization for Rolling Bearing Fault Diagnosis |
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