A Data-Driven Diagnosis Scheme Based on Deep Learning toward Fault Identification of the Hydraulic Piston Pump
The piston pump is the significant source of motive force in a hydraulic transmission system. Owing to the changeable working conditions and complex structural characteristics, multiple friction pairs in the piston pump are prone to wear and failure. An accurate fault diagnosis method is a crucial g...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/11/7/1273 |
| _version_ | 1827732784496508928 |
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| author | Yong Zhu Tao Zhou Shengnan Tang Shouqi Yuan |
| author_facet | Yong Zhu Tao Zhou Shengnan Tang Shouqi Yuan |
| author_sort | Yong Zhu |
| collection | DOAJ |
| description | The piston pump is the significant source of motive force in a hydraulic transmission system. Owing to the changeable working conditions and complex structural characteristics, multiple friction pairs in the piston pump are prone to wear and failure. An accurate fault diagnosis method is a crucial guarantee for system reliability. Deep learning provides a great insight into the intelligent exploration of machinery fault diagnosis. Hyperparameters are very important to construct an effective deep model with good performance. This research fully mines the feature component from vibration signals, and converts the failure recognition into a classification issue via establishing a deep model. Furthermore, Bayesian algorithm is introduced for hyperparameter optimization as it considers prior information. An adaptive convolutional neural network is established for typical failure pattern recognition of an axial piston pump. The proposed method can automatically complete fault classification and represents a higher accuracy by experimental verification. Typical failures of an axial piston pump are intelligently diagnosed with reduced subjectivity and preprocessing knowledge. The proposed method achieves an identification accuracy of more than 98% for five typical conditions of an axial piston pump. |
| first_indexed | 2024-03-11T00:56:56Z |
| format | Article |
| id | doaj.art-9dfff02d54a94370a86d0eeed8f54868 |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-03-11T00:56:56Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-9dfff02d54a94370a86d0eeed8f548682023-11-18T19:57:47ZengMDPI AGJournal of Marine Science and Engineering2077-13122023-06-01117127310.3390/jmse11071273A Data-Driven Diagnosis Scheme Based on Deep Learning toward Fault Identification of the Hydraulic Piston PumpYong Zhu0Tao Zhou1Shengnan Tang2Shouqi Yuan3National Research Center of Pumps, Jiangsu University, Zhenjiang 212013, ChinaNational Research Center of Pumps, Jiangsu University, Zhenjiang 212013, ChinaInstitute of Advanced Manufacturing and Modern Equipment Technology, Jiangsu University, Zhenjiang 212013, ChinaNational Research Center of Pumps, Jiangsu University, Zhenjiang 212013, ChinaThe piston pump is the significant source of motive force in a hydraulic transmission system. Owing to the changeable working conditions and complex structural characteristics, multiple friction pairs in the piston pump are prone to wear and failure. An accurate fault diagnosis method is a crucial guarantee for system reliability. Deep learning provides a great insight into the intelligent exploration of machinery fault diagnosis. Hyperparameters are very important to construct an effective deep model with good performance. This research fully mines the feature component from vibration signals, and converts the failure recognition into a classification issue via establishing a deep model. Furthermore, Bayesian algorithm is introduced for hyperparameter optimization as it considers prior information. An adaptive convolutional neural network is established for typical failure pattern recognition of an axial piston pump. The proposed method can automatically complete fault classification and represents a higher accuracy by experimental verification. Typical failures of an axial piston pump are intelligently diagnosed with reduced subjectivity and preprocessing knowledge. The proposed method achieves an identification accuracy of more than 98% for five typical conditions of an axial piston pump.https://www.mdpi.com/2077-1312/11/7/1273piston pumppattern identificationdeep learninghyperparameter optimization |
| spellingShingle | Yong Zhu Tao Zhou Shengnan Tang Shouqi Yuan A Data-Driven Diagnosis Scheme Based on Deep Learning toward Fault Identification of the Hydraulic Piston Pump Journal of Marine Science and Engineering piston pump pattern identification deep learning hyperparameter optimization |
| title | A Data-Driven Diagnosis Scheme Based on Deep Learning toward Fault Identification of the Hydraulic Piston Pump |
| title_full | A Data-Driven Diagnosis Scheme Based on Deep Learning toward Fault Identification of the Hydraulic Piston Pump |
| title_fullStr | A Data-Driven Diagnosis Scheme Based on Deep Learning toward Fault Identification of the Hydraulic Piston Pump |
| title_full_unstemmed | A Data-Driven Diagnosis Scheme Based on Deep Learning toward Fault Identification of the Hydraulic Piston Pump |
| title_short | A Data-Driven Diagnosis Scheme Based on Deep Learning toward Fault Identification of the Hydraulic Piston Pump |
| title_sort | data driven diagnosis scheme based on deep learning toward fault identification of the hydraulic piston pump |
| topic | piston pump pattern identification deep learning hyperparameter optimization |
| url | https://www.mdpi.com/2077-1312/11/7/1273 |
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