Effective Convolutional Transformer for Highly Accurate Planetary Gearbox Fault Diagnosis
To extract the global temporal correlations and local features together to enhance the accuracy for fault diagnosis, this paper proposes an effective convolutional Transformer (ECT), which can learn the global temporal correlations using Transformer and local features with convolution at the same ti...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2022-01-01
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| Series: | IEEE Open Journal of Instrumentation and Measurement |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9828477/ |
| _version_ | 1797197394996101120 |
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| author | Wenjun Sun Hui Wang Jiawen Xu Yuan Yang Ruqiang Yan |
| author_facet | Wenjun Sun Hui Wang Jiawen Xu Yuan Yang Ruqiang Yan |
| author_sort | Wenjun Sun |
| collection | DOAJ |
| description | To extract the global temporal correlations and local features together to enhance the accuracy for fault diagnosis, this paper proposes an effective convolutional Transformer (ECT), which can learn the global temporal correlations using Transformer and local features with convolution at the same time. The proposed method designs a multi-stage hierarchical structure of Transformer, which utilizes convolutional tokenization to distill dominating sequence features from raw vibration signals while increasing the dimension of token embedding across stages at the same time as that in CNNs. The spatial-reduction attention (SRA) and the linear dimension reduction projections are introduced respectively to Transformer at different stages to reduce the resource consumption of the model. Finally, the proposed method utilizes a sequence pooling strategy on the output of Transformer to eliminate the requirement of the class token and make the model accurate for classification. The specially designed structure makes the model flexible and effective for planetary gearbox fault diagnosis. Experiments performed on planetary gearbox fault simulators indicate that the ECT method has significant effectiveness and high accuracy compared with the state-of-the-art methods for planetary gearbox fault diagnosis. |
| first_indexed | 2024-04-24T06:43:17Z |
| format | Article |
| id | doaj.art-6e624cd5ed034d918703ea3cd64c68c5 |
| institution | Directory Open Access Journal |
| issn | 2768-7236 |
| language | English |
| last_indexed | 2024-04-24T06:43:17Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Instrumentation and Measurement |
| spelling | doaj.art-6e624cd5ed034d918703ea3cd64c68c52024-04-22T20:23:33ZengIEEEIEEE Open Journal of Instrumentation and Measurement2768-72362022-01-0111910.1109/OJIM.2022.31905359828477Effective Convolutional Transformer for Highly Accurate Planetary Gearbox Fault DiagnosisWenjun Sun0https://orcid.org/0000-0001-7802-9063Hui Wang1https://orcid.org/0000-0002-4910-7824Jiawen Xu2https://orcid.org/0000-0002-5398-0394Yuan Yang3https://orcid.org/0000-0003-2266-6682Ruqiang Yan4https://orcid.org/0000-0003-4341-6535School of Instrument Science and Engineering, Southeast University, Nanjing, ChinaSchool of Instrument Science and Engineering, Southeast University, Nanjing, ChinaSchool of Instrument Science and Engineering, Southeast University, Nanjing, ChinaSchool of Instrument Science and Engineering, Southeast University, Nanjing, ChinaSchool of Instrument Science and Engineering, Southeast University, Nanjing, ChinaTo extract the global temporal correlations and local features together to enhance the accuracy for fault diagnosis, this paper proposes an effective convolutional Transformer (ECT), which can learn the global temporal correlations using Transformer and local features with convolution at the same time. The proposed method designs a multi-stage hierarchical structure of Transformer, which utilizes convolutional tokenization to distill dominating sequence features from raw vibration signals while increasing the dimension of token embedding across stages at the same time as that in CNNs. The spatial-reduction attention (SRA) and the linear dimension reduction projections are introduced respectively to Transformer at different stages to reduce the resource consumption of the model. Finally, the proposed method utilizes a sequence pooling strategy on the output of Transformer to eliminate the requirement of the class token and make the model accurate for classification. The specially designed structure makes the model flexible and effective for planetary gearbox fault diagnosis. Experiments performed on planetary gearbox fault simulators indicate that the ECT method has significant effectiveness and high accuracy compared with the state-of-the-art methods for planetary gearbox fault diagnosis.https://ieeexplore.ieee.org/document/9828477/Convolutional tokenizationfault diagnosisspatial-reduction attentionsequence poolingtransformer |
| spellingShingle | Wenjun Sun Hui Wang Jiawen Xu Yuan Yang Ruqiang Yan Effective Convolutional Transformer for Highly Accurate Planetary Gearbox Fault Diagnosis IEEE Open Journal of Instrumentation and Measurement Convolutional tokenization fault diagnosis spatial-reduction attention sequence pooling transformer |
| title | Effective Convolutional Transformer for Highly Accurate Planetary Gearbox Fault Diagnosis |
| title_full | Effective Convolutional Transformer for Highly Accurate Planetary Gearbox Fault Diagnosis |
| title_fullStr | Effective Convolutional Transformer for Highly Accurate Planetary Gearbox Fault Diagnosis |
| title_full_unstemmed | Effective Convolutional Transformer for Highly Accurate Planetary Gearbox Fault Diagnosis |
| title_short | Effective Convolutional Transformer for Highly Accurate Planetary Gearbox Fault Diagnosis |
| title_sort | effective convolutional transformer for highly accurate planetary gearbox fault diagnosis |
| topic | Convolutional tokenization fault diagnosis spatial-reduction attention sequence pooling transformer |
| url | https://ieeexplore.ieee.org/document/9828477/ |
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