Planetary Gearboxes Fault Diagnosis Based on Markov Transition Fields and SE-ResNet
The working conditions of planetary gearboxes are complex, and their structural couplings are strong, leading to low reliability. Traditional deep neural networks often struggle with feature learning in noisy environments, and their reliance on one-dimensional signals as input fails to capture the i...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-11-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/24/23/7540 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846123860274970624 |
|---|---|
| author | Yanyan Liu Tongxin Gao Wenxu Wu Yongquan Sun |
| author_facet | Yanyan Liu Tongxin Gao Wenxu Wu Yongquan Sun |
| author_sort | Yanyan Liu |
| collection | DOAJ |
| description | The working conditions of planetary gearboxes are complex, and their structural couplings are strong, leading to low reliability. Traditional deep neural networks often struggle with feature learning in noisy environments, and their reliance on one-dimensional signals as input fails to capture the interrelationships between data points. To address these challenges, we proposed a fault diagnosis method for planetary gearboxes that integrates Markov transition fields (MTFs) and a residual attention mechanism. The MTF was employed to encode one-dimensional signals into feature maps, which were then fed into a residual networks (ResNet) architecture. To enhance the network’s ability to focus on important features, we embedded the squeeze-and-excitation (SE) channel attention mechanism into the ResNet34 network, creating a SE-ResNet model. This model was trained to effectively extract and classify features. The developed method was validated using a specific dataset and achieved an accuracy of about 98.1%. The results demonstrate the effectiveness and reliability of the developed method in diagnosing faults in planetary gearboxes under strong noise conditions. |
| format | Article |
| id | doaj-art-c2298dc9642e4f0a94ad43f09d55e4dd |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-c2298dc9642e4f0a94ad43f09d55e4dd2024-12-13T16:31:52ZengMDPI AGSensors1424-82202024-11-012423754010.3390/s24237540Planetary Gearboxes Fault Diagnosis Based on Markov Transition Fields and SE-ResNetYanyan Liu0Tongxin Gao1Wenxu Wu2Yongquan Sun3School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaSchool of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaInstitute of Sensor and Reliability Engineering, Harbin University of Science and Technology, Harbin 150080, ChinaInstitute of Sensor and Reliability Engineering, Harbin University of Science and Technology, Harbin 150080, ChinaThe working conditions of planetary gearboxes are complex, and their structural couplings are strong, leading to low reliability. Traditional deep neural networks often struggle with feature learning in noisy environments, and their reliance on one-dimensional signals as input fails to capture the interrelationships between data points. To address these challenges, we proposed a fault diagnosis method for planetary gearboxes that integrates Markov transition fields (MTFs) and a residual attention mechanism. The MTF was employed to encode one-dimensional signals into feature maps, which were then fed into a residual networks (ResNet) architecture. To enhance the network’s ability to focus on important features, we embedded the squeeze-and-excitation (SE) channel attention mechanism into the ResNet34 network, creating a SE-ResNet model. This model was trained to effectively extract and classify features. The developed method was validated using a specific dataset and achieved an accuracy of about 98.1%. The results demonstrate the effectiveness and reliability of the developed method in diagnosing faults in planetary gearboxes under strong noise conditions.https://www.mdpi.com/1424-8220/24/23/7540Markov transfer fieldsresidual networksgearbox fault diagnosisattention mechanisms |
| spellingShingle | Yanyan Liu Tongxin Gao Wenxu Wu Yongquan Sun Planetary Gearboxes Fault Diagnosis Based on Markov Transition Fields and SE-ResNet Sensors Markov transfer fields residual networks gearbox fault diagnosis attention mechanisms |
| title | Planetary Gearboxes Fault Diagnosis Based on Markov Transition Fields and SE-ResNet |
| title_full | Planetary Gearboxes Fault Diagnosis Based on Markov Transition Fields and SE-ResNet |
| title_fullStr | Planetary Gearboxes Fault Diagnosis Based on Markov Transition Fields and SE-ResNet |
| title_full_unstemmed | Planetary Gearboxes Fault Diagnosis Based on Markov Transition Fields and SE-ResNet |
| title_short | Planetary Gearboxes Fault Diagnosis Based on Markov Transition Fields and SE-ResNet |
| title_sort | planetary gearboxes fault diagnosis based on markov transition fields and se resnet |
| topic | Markov transfer fields residual networks gearbox fault diagnosis attention mechanisms |
| url | https://www.mdpi.com/1424-8220/24/23/7540 |
| work_keys_str_mv | AT yanyanliu planetarygearboxesfaultdiagnosisbasedonmarkovtransitionfieldsandseresnet AT tongxingao planetarygearboxesfaultdiagnosisbasedonmarkovtransitionfieldsandseresnet AT wenxuwu planetarygearboxesfaultdiagnosisbasedonmarkovtransitionfieldsandseresnet AT yongquansun planetarygearboxesfaultdiagnosisbasedonmarkovtransitionfieldsandseresnet |