A Parameter-Optimized DBN Using GOA and Its Application in Fault Diagnosis of Gearbox
Aiming at the problems of poor self-adaptive ability in traditional feature extraction methods and weak generalization ability in single classifier under big data, an internal parameter-optimized Deep Belief Network (DBN) method based on grasshopper optimization algorithm (GOA) is proposed. First, t...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2020/4294095 |
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| _version_ | 1849691219089686528 |
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| author | Jingbo Gai Junxian Shen He Wang Yifan Hu |
| author_facet | Jingbo Gai Junxian Shen He Wang Yifan Hu |
| author_sort | Jingbo Gai |
| collection | DOAJ |
| description | Aiming at the problems of poor self-adaptive ability in traditional feature extraction methods and weak generalization ability in single classifier under big data, an internal parameter-optimized Deep Belief Network (DBN) method based on grasshopper optimization algorithm (GOA) is proposed. First, the minimum Root Mean Square Error (RMSE) in the network training is taken as the fitness function, in which GOA is used to search for the optimal parameter combination of DBN. After that the learning rate and the number of batch learning in DBN which have great influence on the training error would be properly selected. At the same time, the optimal structure distribution of DBN is given through comparison. Then, FFT and linear normalization are introduced to process the original vibration signal of the gearbox, preprocess the data from multiple sensors and construct the input samples for DBN. Finally, combining with deep learning featured by powerful self-adaptive feature extraction and nonlinear mapping capabilities, the obtained samples are input into DBN for training, and the fault diagnosis model for gearbox based on DBN would be established. After several tests with the remaining samples, the diagnosis rate of the model could reach over 99.5%, which is far better than the traditional fault diagnosis method based on feature extraction and pattern recognition. The experimental results show that this method could effectively improve the self-adaptive feature extraction ability of the model as well as its accuracy of fault diagnosis, which has better generalization performance. |
| format | Article |
| id | doaj-art-e2ce9cb147784cf5b54dc945e33a99a4 |
| institution | DOAJ |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-e2ce9cb147784cf5b54dc945e33a99a42025-08-20T03:21:06ZengWileyShock and Vibration1070-96221875-92032020-01-01202010.1155/2020/42940954294095A Parameter-Optimized DBN Using GOA and Its Application in Fault Diagnosis of GearboxJingbo Gai0Junxian Shen1He Wang2Yifan Hu3College of Aerospace and Civil Engineering, Harbin Engineering University, Harbin 150001, Heilongjiang Province, ChinaCollege of Aerospace and Civil Engineering, Harbin Engineering University, Harbin 150001, Heilongjiang Province, ChinaCollege of Aerospace and Civil Engineering, Harbin Engineering University, Harbin 150001, Heilongjiang Province, ChinaSchool of Electrical Engineering and Automation, Harbifn Institute of Technology, Harbin 150001, Heilongjiang Province, ChinaAiming at the problems of poor self-adaptive ability in traditional feature extraction methods and weak generalization ability in single classifier under big data, an internal parameter-optimized Deep Belief Network (DBN) method based on grasshopper optimization algorithm (GOA) is proposed. First, the minimum Root Mean Square Error (RMSE) in the network training is taken as the fitness function, in which GOA is used to search for the optimal parameter combination of DBN. After that the learning rate and the number of batch learning in DBN which have great influence on the training error would be properly selected. At the same time, the optimal structure distribution of DBN is given through comparison. Then, FFT and linear normalization are introduced to process the original vibration signal of the gearbox, preprocess the data from multiple sensors and construct the input samples for DBN. Finally, combining with deep learning featured by powerful self-adaptive feature extraction and nonlinear mapping capabilities, the obtained samples are input into DBN for training, and the fault diagnosis model for gearbox based on DBN would be established. After several tests with the remaining samples, the diagnosis rate of the model could reach over 99.5%, which is far better than the traditional fault diagnosis method based on feature extraction and pattern recognition. The experimental results show that this method could effectively improve the self-adaptive feature extraction ability of the model as well as its accuracy of fault diagnosis, which has better generalization performance.http://dx.doi.org/10.1155/2020/4294095 |
| spellingShingle | Jingbo Gai Junxian Shen He Wang Yifan Hu A Parameter-Optimized DBN Using GOA and Its Application in Fault Diagnosis of Gearbox Shock and Vibration |
| title | A Parameter-Optimized DBN Using GOA and Its Application in Fault Diagnosis of Gearbox |
| title_full | A Parameter-Optimized DBN Using GOA and Its Application in Fault Diagnosis of Gearbox |
| title_fullStr | A Parameter-Optimized DBN Using GOA and Its Application in Fault Diagnosis of Gearbox |
| title_full_unstemmed | A Parameter-Optimized DBN Using GOA and Its Application in Fault Diagnosis of Gearbox |
| title_short | A Parameter-Optimized DBN Using GOA and Its Application in Fault Diagnosis of Gearbox |
| title_sort | parameter optimized dbn using goa and its application in fault diagnosis of gearbox |
| url | http://dx.doi.org/10.1155/2020/4294095 |
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