Fault Diagnosis of Planetary Gearbox based on 1-DCNN
Traditional machine learning methods have disadvantages such as low recognition rate and complicated feature extraction operations in the planetary gearbox fault diagnosis. In order to improve the diagnosis efficiency of planetary gearboxes， a fault diagnosis method based on one-dimensional deep con...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Journal of Mechanical Transmission
2020-11-01
|
| Series: | Jixie chuandong |
| Subjects: | |
| Online Access: | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2020.11.021 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841547167999721472 |
|---|---|
| author | Xuanyi Xue Xinyu Pang |
| author_facet | Xuanyi Xue Xinyu Pang |
| author_sort | Xuanyi Xue |
| collection | DOAJ |
| description | Traditional machine learning methods have disadvantages such as low recognition rate and complicated feature extraction operations in the planetary gearbox fault diagnosis. In order to improve the diagnosis efficiency of planetary gearboxes， a fault diagnosis method based on one-dimensional deep convolutional neural network （1-DCNN） is proposed， and the original signals are directly input to the network for diagnosis. The accuracy of diagnosing five kinds of fault signals of planetary gear of planetary gear box can reach 100%， and the diagnostic accuracy and efficiency are better than other commonly used algorithms. |
| format | Article |
| id | doaj-art-6cde89ab979a468bbee9f2fd8020e0fc |
| institution | Kabale University |
| issn | 1004-2539 |
| language | zho |
| publishDate | 2020-11-01 |
| publisher | Editorial Office of Journal of Mechanical Transmission |
| record_format | Article |
| series | Jixie chuandong |
| spelling | doaj-art-6cde89ab979a468bbee9f2fd8020e0fc2025-01-10T14:54:57ZzhoEditorial Office of Journal of Mechanical TransmissionJixie chuandong1004-25392020-11-014412713329792295Fault Diagnosis of Planetary Gearbox based on 1-DCNNXuanyi XueXinyu PangTraditional machine learning methods have disadvantages such as low recognition rate and complicated feature extraction operations in the planetary gearbox fault diagnosis. In order to improve the diagnosis efficiency of planetary gearboxes， a fault diagnosis method based on one-dimensional deep convolutional neural network （1-DCNN） is proposed， and the original signals are directly input to the network for diagnosis. The accuracy of diagnosing five kinds of fault signals of planetary gear of planetary gear box can reach 100%， and the diagnostic accuracy and efficiency are better than other commonly used algorithms.http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2020.11.0211-DCNN intelligent diagnosisFeature extractionPlanetary gearbox |
| spellingShingle | Xuanyi Xue Xinyu Pang Fault Diagnosis of Planetary Gearbox based on 1-DCNN Jixie chuandong 1-DCNN intelligent diagnosis Feature extraction Planetary gearbox |
| title | Fault Diagnosis of Planetary Gearbox based on 1-DCNN |
| title_full | Fault Diagnosis of Planetary Gearbox based on 1-DCNN |
| title_fullStr | Fault Diagnosis of Planetary Gearbox based on 1-DCNN |
| title_full_unstemmed | Fault Diagnosis of Planetary Gearbox based on 1-DCNN |
| title_short | Fault Diagnosis of Planetary Gearbox based on 1-DCNN |
| title_sort | fault diagnosis of planetary gearbox based on 1 dcnn |
| topic | 1-DCNN intelligent diagnosis Feature extraction Planetary gearbox |
| url | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2020.11.021 |
| work_keys_str_mv | AT xuanyixue faultdiagnosisofplanetarygearboxbasedon1dcnn AT xinyupang faultdiagnosisofplanetarygearboxbasedon1dcnn |