Research on an Improved Auxiliary Classifier Wasserstein Generative Adversarial Network with Gradient Penalty Fault Diagnosis Method for Tilting Pad Bearing of Rotating Equipment
The research on fault diagnosis methods based on generative adversarial networks has achieved fruitful results, but most of the research objects are rolling bearings or gears, and the model test data are almost all derived from laboratory bench test data. In the industrial Internet environment, equi...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Lubricants |
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| Online Access: | https://www.mdpi.com/2075-4442/11/10/423 |
| _version_ | 1797573216902840320 |
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| author | Chunlei Zhou Qingfeng Wang Yang Xiao Wang Xiao Yue Shu |
| author_facet | Chunlei Zhou Qingfeng Wang Yang Xiao Wang Xiao Yue Shu |
| author_sort | Chunlei Zhou |
| collection | DOAJ |
| description | The research on fault diagnosis methods based on generative adversarial networks has achieved fruitful results, but most of the research objects are rolling bearings or gears, and the model test data are almost all derived from laboratory bench test data. In the industrial Internet environment, equipment-fault diagnosis is faced with the characteristics of large amounts of data, unbalanced data samples, and inconsistent data file lengths. Moreover, there are few research results on the fault diagnosis of rotor systems composed of shafts, impellers or blades, couplings, and tilting pad bearings. There are still shortcomings in the operational risk evaluation of rotor systems. In order to ensure the reliability and safety of rotor systems, an Improved Auxiliary Classifier Wasserstein Generative Adversarial Network with Gradient Penalty (IACWGAN-GP) model is constructed, a fault diagnosis method based on IACWGAN-GP for tilting pad bearings is proposed, and an intelligent fault diagnosis system platform for equipment in an industrial Internet environment is built. The verification results of engineering case data show that the fault diagnosis model based on IACWGAN-GP can adapt to any length of sequential data files, and the automatic identification accuracy of early faults in tilting pad bearings reaches 98.7%. |
| first_indexed | 2024-03-10T21:06:47Z |
| format | Article |
| id | doaj.art-4dccd4d196f74cbd81c0407ce7d09c15 |
| institution | Directory Open Access Journal |
| issn | 2075-4442 |
| language | English |
| last_indexed | 2024-03-10T21:06:47Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Lubricants |
| spelling | doaj.art-4dccd4d196f74cbd81c0407ce7d09c152023-11-19T17:07:38ZengMDPI AGLubricants2075-44422023-10-01111042310.3390/lubricants11100423Research on an Improved Auxiliary Classifier Wasserstein Generative Adversarial Network with Gradient Penalty Fault Diagnosis Method for Tilting Pad Bearing of Rotating EquipmentChunlei Zhou0Qingfeng Wang1Yang Xiao2Wang Xiao3Yue Shu4School of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing 100029, ChinaSchool of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing 100029, ChinaSchool of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing 100029, ChinaWestern Branch of National Pipe Network Group United Pipeline Company Ltd., Urumqi 830013, ChinaHefei General Machinery Research Institute Company Ltd., Hefei 230031, ChinaThe research on fault diagnosis methods based on generative adversarial networks has achieved fruitful results, but most of the research objects are rolling bearings or gears, and the model test data are almost all derived from laboratory bench test data. In the industrial Internet environment, equipment-fault diagnosis is faced with the characteristics of large amounts of data, unbalanced data samples, and inconsistent data file lengths. Moreover, there are few research results on the fault diagnosis of rotor systems composed of shafts, impellers or blades, couplings, and tilting pad bearings. There are still shortcomings in the operational risk evaluation of rotor systems. In order to ensure the reliability and safety of rotor systems, an Improved Auxiliary Classifier Wasserstein Generative Adversarial Network with Gradient Penalty (IACWGAN-GP) model is constructed, a fault diagnosis method based on IACWGAN-GP for tilting pad bearings is proposed, and an intelligent fault diagnosis system platform for equipment in an industrial Internet environment is built. The verification results of engineering case data show that the fault diagnosis model based on IACWGAN-GP can adapt to any length of sequential data files, and the automatic identification accuracy of early faults in tilting pad bearings reaches 98.7%.https://www.mdpi.com/2075-4442/11/10/423fault diagnosisrisk evaluationimproved auxiliary classifier Wasserstein generative adversarial network with gradient penaltyindustrial Internetrotor system |
| spellingShingle | Chunlei Zhou Qingfeng Wang Yang Xiao Wang Xiao Yue Shu Research on an Improved Auxiliary Classifier Wasserstein Generative Adversarial Network with Gradient Penalty Fault Diagnosis Method for Tilting Pad Bearing of Rotating Equipment Lubricants fault diagnosis risk evaluation improved auxiliary classifier Wasserstein generative adversarial network with gradient penalty industrial Internet rotor system |
| title | Research on an Improved Auxiliary Classifier Wasserstein Generative Adversarial Network with Gradient Penalty Fault Diagnosis Method for Tilting Pad Bearing of Rotating Equipment |
| title_full | Research on an Improved Auxiliary Classifier Wasserstein Generative Adversarial Network with Gradient Penalty Fault Diagnosis Method for Tilting Pad Bearing of Rotating Equipment |
| title_fullStr | Research on an Improved Auxiliary Classifier Wasserstein Generative Adversarial Network with Gradient Penalty Fault Diagnosis Method for Tilting Pad Bearing of Rotating Equipment |
| title_full_unstemmed | Research on an Improved Auxiliary Classifier Wasserstein Generative Adversarial Network with Gradient Penalty Fault Diagnosis Method for Tilting Pad Bearing of Rotating Equipment |
| title_short | Research on an Improved Auxiliary Classifier Wasserstein Generative Adversarial Network with Gradient Penalty Fault Diagnosis Method for Tilting Pad Bearing of Rotating Equipment |
| title_sort | research on an improved auxiliary classifier wasserstein generative adversarial network with gradient penalty fault diagnosis method for tilting pad bearing of rotating equipment |
| topic | fault diagnosis risk evaluation improved auxiliary classifier Wasserstein generative adversarial network with gradient penalty industrial Internet rotor system |
| url | https://www.mdpi.com/2075-4442/11/10/423 |
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