Short-term Fault Prediction Method for Bearing Based on SA-TCN
Purposes Bearing is one of the core components in the manufacturing industry. Its health status determines the safety of the host. Short-term failure prediction can effectively ensure the smooth progress of the industrial production process. Methods In order to solve the end-to-end problem, a tempor...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Editorial Office of Journal of Taiyuan University of Technology
2024-01-01
|
| Series: | Taiyuan Ligong Daxue xuebao |
| Subjects: | |
| Online Access: | https://tyutjournal.tyut.edu.cn/englishpaper/show-2261.html |
| _version_ | 1797208297133047808 |
|---|---|
| author | Siyuan WANG Junghui CHEN Kai GU Mifeng REN Gaowei YAN |
| author_facet | Siyuan WANG Junghui CHEN Kai GU Mifeng REN Gaowei YAN |
| author_sort | Siyuan WANG |
| collection | DOAJ |
| description | Purposes Bearing is one of the core components in the manufacturing industry. Its health status determines the safety of the host. Short-term failure prediction can effectively ensure the smooth progress of the industrial production process. Methods In order to solve the end-to-end problem, a temporal convolutional network (TCN) based short-term fault prediction strategy was proposed. The network could directly output the types of failure that would eventually occur in the bearing and the degradation stage that would be in the next moment through the data monitored at the current moment. In addition, soft threshold with attention mechanism is proposed to solve the problem of background noise in the working environment of bearings or noise interference in the process of data acquisition. During the short-term fault prediction process, the attention mechanism adaptively generates a soft threshold according to the prediction target of the TCN network, and the soft threshold acts on the spatiotemporal features extracted by the TCN to achieve the purpose of reducing noise impact. Findings The experimental results show that the proposed algorithm has high accuracy, which verifies the effectiveness and high practical engineering application value of the proposed algorithm. |
| first_indexed | 2024-04-24T09:36:34Z |
| format | Article |
| id | doaj.art-7f0651eb0e0a4c21bc49652b32f075ab |
| institution | Directory Open Access Journal |
| issn | 1007-9432 |
| language | English |
| last_indexed | 2024-04-24T09:36:34Z |
| publishDate | 2024-01-01 |
| publisher | Editorial Office of Journal of Taiyuan University of Technology |
| record_format | Article |
| series | Taiyuan Ligong Daxue xuebao |
| spelling | doaj.art-7f0651eb0e0a4c21bc49652b32f075ab2024-04-15T09:17:22ZengEditorial Office of Journal of Taiyuan University of TechnologyTaiyuan Ligong Daxue xuebao1007-94322024-01-0155121422210.16355/j.tyut.1007-9432.202301171007-9432(2024)01-0214-09Short-term Fault Prediction Method for Bearing Based on SA-TCNSiyuan WANG0Junghui CHEN1Kai GU2Mifeng REN3Gaowei YAN4College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, ChinaDepartment of Chemical Engineering, Chung Yuan Christian University, Taoyuan 320-338, ChinaState Key Laboratory of Nuclear Power Safety Monitoring Technology and Equipment, China Nuclear Power Engineering Co., Ltd., Shenzhen 518172, ChinaCollege of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, ChinaCollege of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, ChinaPurposes Bearing is one of the core components in the manufacturing industry. Its health status determines the safety of the host. Short-term failure prediction can effectively ensure the smooth progress of the industrial production process. Methods In order to solve the end-to-end problem, a temporal convolutional network (TCN) based short-term fault prediction strategy was proposed. The network could directly output the types of failure that would eventually occur in the bearing and the degradation stage that would be in the next moment through the data monitored at the current moment. In addition, soft threshold with attention mechanism is proposed to solve the problem of background noise in the working environment of bearings or noise interference in the process of data acquisition. During the short-term fault prediction process, the attention mechanism adaptively generates a soft threshold according to the prediction target of the TCN network, and the soft threshold acts on the spatiotemporal features extracted by the TCN to achieve the purpose of reducing noise impact. Findings The experimental results show that the proposed algorithm has high accuracy, which verifies the effectiveness and high practical engineering application value of the proposed algorithm.https://tyutjournal.tyut.edu.cn/englishpaper/show-2261.htmlshort-term failure predictiontemporal convolutional networkbearingattention mechanism |
| spellingShingle | Siyuan WANG Junghui CHEN Kai GU Mifeng REN Gaowei YAN Short-term Fault Prediction Method for Bearing Based on SA-TCN Taiyuan Ligong Daxue xuebao short-term failure prediction temporal convolutional network bearing attention mechanism |
| title | Short-term Fault Prediction Method for Bearing Based on SA-TCN |
| title_full | Short-term Fault Prediction Method for Bearing Based on SA-TCN |
| title_fullStr | Short-term Fault Prediction Method for Bearing Based on SA-TCN |
| title_full_unstemmed | Short-term Fault Prediction Method for Bearing Based on SA-TCN |
| title_short | Short-term Fault Prediction Method for Bearing Based on SA-TCN |
| title_sort | short term fault prediction method for bearing based on sa tcn |
| topic | short-term failure prediction temporal convolutional network bearing attention mechanism |
| url | https://tyutjournal.tyut.edu.cn/englishpaper/show-2261.html |
| work_keys_str_mv | AT siyuanwang shorttermfaultpredictionmethodforbearingbasedonsatcn AT junghuichen shorttermfaultpredictionmethodforbearingbasedonsatcn AT kaigu shorttermfaultpredictionmethodforbearingbasedonsatcn AT mifengren shorttermfaultpredictionmethodforbearingbasedonsatcn AT gaoweiyan shorttermfaultpredictionmethodforbearingbasedonsatcn |