Research on the Initial Fault Prediction Method of Rolling Bearings Based on DCAE-TCN Transfer Learning
In actual working conditions, the initial faults of rolling bearings are difficult to effectively predict due to the lack of evolution knowledge, weak fault information, and strong noise interference. In this paper, a rolling bearing initial fault prediction model that is based on transfer learning...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
|
| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2021/5587756 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832567467893850112 |
|---|---|
| author | Huaitao Shi Yajun Shang Xiaochen Zhang Yinghan Tang |
| author_facet | Huaitao Shi Yajun Shang Xiaochen Zhang Yinghan Tang |
| author_sort | Huaitao Shi |
| collection | DOAJ |
| description | In actual working conditions, the initial faults of rolling bearings are difficult to effectively predict due to the lack of evolution knowledge, weak fault information, and strong noise interference. In this paper, a rolling bearing initial fault prediction model that is based on transfer learning and the DCAE-TCN is presented. Firstly, a deep autoencoder (DAE as the first two hidden layers and CAE as the last hidden layer) is used to extract fault features from the rolling bearing vibration signal data. Then, the balanced distributed adaptation (BDA) is used to minimise the distribution difference and class spacing between extracted fault features, and a common feature set is constructed. The temporal features of the original vibration signal in the target domain are extracted using the advantages of the TCN. The experiments are conducted on the publicly available XJTU-SY dataset. The experimental results show that the proposed method can effectively learn the transferable features and compensate the differences between the source and target domains and has a promising application with higher accuracy and robustness for the prediction of early failures of rolling bearings. |
| format | Article |
| id | doaj-art-a4921e61e2104fb78b01bf172563e571 |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-a4921e61e2104fb78b01bf172563e5712025-02-03T01:01:25ZengWileyShock and Vibration1070-96221875-92032021-01-01202110.1155/2021/55877565587756Research on the Initial Fault Prediction Method of Rolling Bearings Based on DCAE-TCN Transfer LearningHuaitao Shi0Yajun Shang1Xiaochen Zhang2Yinghan Tang3School of Mechanical Engineering, Shenyang Jianzhu University, Shenyang 110168, ChinaSchool of Mechanical Engineering, Shenyang Jianzhu University, Shenyang 110168, ChinaSchool of Mechanical Engineering, Shenyang Jianzhu University, Shenyang 110168, ChinaBourns College of Engineering, University of California Riverside, Los Angeles 90001, CA, USAIn actual working conditions, the initial faults of rolling bearings are difficult to effectively predict due to the lack of evolution knowledge, weak fault information, and strong noise interference. In this paper, a rolling bearing initial fault prediction model that is based on transfer learning and the DCAE-TCN is presented. Firstly, a deep autoencoder (DAE as the first two hidden layers and CAE as the last hidden layer) is used to extract fault features from the rolling bearing vibration signal data. Then, the balanced distributed adaptation (BDA) is used to minimise the distribution difference and class spacing between extracted fault features, and a common feature set is constructed. The temporal features of the original vibration signal in the target domain are extracted using the advantages of the TCN. The experiments are conducted on the publicly available XJTU-SY dataset. The experimental results show that the proposed method can effectively learn the transferable features and compensate the differences between the source and target domains and has a promising application with higher accuracy and robustness for the prediction of early failures of rolling bearings.http://dx.doi.org/10.1155/2021/5587756 |
| spellingShingle | Huaitao Shi Yajun Shang Xiaochen Zhang Yinghan Tang Research on the Initial Fault Prediction Method of Rolling Bearings Based on DCAE-TCN Transfer Learning Shock and Vibration |
| title | Research on the Initial Fault Prediction Method of Rolling Bearings Based on DCAE-TCN Transfer Learning |
| title_full | Research on the Initial Fault Prediction Method of Rolling Bearings Based on DCAE-TCN Transfer Learning |
| title_fullStr | Research on the Initial Fault Prediction Method of Rolling Bearings Based on DCAE-TCN Transfer Learning |
| title_full_unstemmed | Research on the Initial Fault Prediction Method of Rolling Bearings Based on DCAE-TCN Transfer Learning |
| title_short | Research on the Initial Fault Prediction Method of Rolling Bearings Based on DCAE-TCN Transfer Learning |
| title_sort | research on the initial fault prediction method of rolling bearings based on dcae tcn transfer learning |
| url | http://dx.doi.org/10.1155/2021/5587756 |
| work_keys_str_mv | AT huaitaoshi researchontheinitialfaultpredictionmethodofrollingbearingsbasedondcaetcntransferlearning AT yajunshang researchontheinitialfaultpredictionmethodofrollingbearingsbasedondcaetcntransferlearning AT xiaochenzhang researchontheinitialfaultpredictionmethodofrollingbearingsbasedondcaetcntransferlearning AT yinghantang researchontheinitialfaultpredictionmethodofrollingbearingsbasedondcaetcntransferlearning |