Numerical Simulation Data-Aided Domain-Adaptive Generalization Method for Fault Diagnosis
In order to deal with the cross-domain distribution offset problem in mechanical fault diagnosis under different operating conditions. Domain-adaptive (DA) methods, such as domain adversarial neural networks (DANNs), maximum mean discrepancy (MMD), and correlation alignment (CORAL), have been advanc...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/11/3482 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850129285043453952 |
|---|---|
| author | Tao Yan Jianchun Guo Yuan Zhou Lixia Zhu Bo Fang Jiawei Xiang |
| author_facet | Tao Yan Jianchun Guo Yuan Zhou Lixia Zhu Bo Fang Jiawei Xiang |
| author_sort | Tao Yan |
| collection | DOAJ |
| description | In order to deal with the cross-domain distribution offset problem in mechanical fault diagnosis under different operating conditions. Domain-adaptive (DA) methods, such as domain adversarial neural networks (DANNs), maximum mean discrepancy (MMD), and correlation alignment (CORAL), have been advanced in recent years, producing notable outcomes. However, these techniques rely on the accessibility of target data, restricting their use in real-time fault diagnosis applications. To address this issue, effectively extracting fault features in the source domain and generalizing them to unseen target tasks becomes a viable strategy in machinery fault detection. A fault diagnosis domain generalization method using numerical simulation data is proposed. Firstly, the finite element model (FEM) is used to generate simulation data under certain working conditions as an auxiliary domain. Secondly, this auxiliary domain is integrated with measurement data obtained under different operating conditions to form a multi-source domain. Finally, adversarial training is conducted on the multi-source domain to learn domain-invariant features, thereby enhancing the model’s generalization capability for out-of-distribution data. Experimental results on bearings and gears show that the generalization performance of the proposed method is better than that of the existing baseline methods, with the average accuracy improved by 2.83% and 8.9%, respectively. |
| format | Article |
| id | doaj-art-dd7d8740bc584d128fdabe042f5d3d80 |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-dd7d8740bc584d128fdabe042f5d3d802025-08-20T02:33:02ZengMDPI AGSensors1424-82202025-05-012511348210.3390/s25113482Numerical Simulation Data-Aided Domain-Adaptive Generalization Method for Fault DiagnosisTao Yan0Jianchun Guo1Yuan Zhou2Lixia Zhu3Bo Fang4Jiawei Xiang5College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou 325035, ChinaCollege of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou 325035, ChinaCollege of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou 325035, ChinaCollege of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou 325035, ChinaNingbo Puer Mechanical Electrical Manufacturing Co., Ltd., Yuyao 315420, ChinaCollege of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou 325035, ChinaIn order to deal with the cross-domain distribution offset problem in mechanical fault diagnosis under different operating conditions. Domain-adaptive (DA) methods, such as domain adversarial neural networks (DANNs), maximum mean discrepancy (MMD), and correlation alignment (CORAL), have been advanced in recent years, producing notable outcomes. However, these techniques rely on the accessibility of target data, restricting their use in real-time fault diagnosis applications. To address this issue, effectively extracting fault features in the source domain and generalizing them to unseen target tasks becomes a viable strategy in machinery fault detection. A fault diagnosis domain generalization method using numerical simulation data is proposed. Firstly, the finite element model (FEM) is used to generate simulation data under certain working conditions as an auxiliary domain. Secondly, this auxiliary domain is integrated with measurement data obtained under different operating conditions to form a multi-source domain. Finally, adversarial training is conducted on the multi-source domain to learn domain-invariant features, thereby enhancing the model’s generalization capability for out-of-distribution data. Experimental results on bearings and gears show that the generalization performance of the proposed method is better than that of the existing baseline methods, with the average accuracy improved by 2.83% and 8.9%, respectively.https://www.mdpi.com/1424-8220/25/11/3482finite element modeldomain generalizationfault diagnosisdomain adaptive |
| spellingShingle | Tao Yan Jianchun Guo Yuan Zhou Lixia Zhu Bo Fang Jiawei Xiang Numerical Simulation Data-Aided Domain-Adaptive Generalization Method for Fault Diagnosis Sensors finite element model domain generalization fault diagnosis domain adaptive |
| title | Numerical Simulation Data-Aided Domain-Adaptive Generalization Method for Fault Diagnosis |
| title_full | Numerical Simulation Data-Aided Domain-Adaptive Generalization Method for Fault Diagnosis |
| title_fullStr | Numerical Simulation Data-Aided Domain-Adaptive Generalization Method for Fault Diagnosis |
| title_full_unstemmed | Numerical Simulation Data-Aided Domain-Adaptive Generalization Method for Fault Diagnosis |
| title_short | Numerical Simulation Data-Aided Domain-Adaptive Generalization Method for Fault Diagnosis |
| title_sort | numerical simulation data aided domain adaptive generalization method for fault diagnosis |
| topic | finite element model domain generalization fault diagnosis domain adaptive |
| url | https://www.mdpi.com/1424-8220/25/11/3482 |
| work_keys_str_mv | AT taoyan numericalsimulationdataaideddomainadaptivegeneralizationmethodforfaultdiagnosis AT jianchunguo numericalsimulationdataaideddomainadaptivegeneralizationmethodforfaultdiagnosis AT yuanzhou numericalsimulationdataaideddomainadaptivegeneralizationmethodforfaultdiagnosis AT lixiazhu numericalsimulationdataaideddomainadaptivegeneralizationmethodforfaultdiagnosis AT bofang numericalsimulationdataaideddomainadaptivegeneralizationmethodforfaultdiagnosis AT jiaweixiang numericalsimulationdataaideddomainadaptivegeneralizationmethodforfaultdiagnosis |