Research on bearing fault feature transfer diagnosis based on balanced distribution adaptation under feature fusion
In practical industrial applications, the operating conditions of bearings frequently change, posing significant challenges for reliable fault diagnosis. Traditional machine learning methods, which rely on the assumption of independent and identically distributed samples, often experience a signific...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2025-06-01
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| Series: | Advances in Mechanical Engineering |
| Online Access: | https://doi.org/10.1177/16878132251348366 |
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| _version_ | 1849422040360026112 |
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| author | Lulu Wang Yongqi Li Chunyi Zhang Ralph Gerard Sangalang |
| author_facet | Lulu Wang Yongqi Li Chunyi Zhang Ralph Gerard Sangalang |
| author_sort | Lulu Wang |
| collection | DOAJ |
| description | In practical industrial applications, the operating conditions of bearings frequently change, posing significant challenges for reliable fault diagnosis. Traditional machine learning methods, which rely on the assumption of independent and identically distributed samples, often experience a significant decline in diagnostic accuracy under such variable conditions. To address this issue, this paper proposes a bearing fault transfer diagnosis method that combines the Balanced Distribution Adaptation (BDA) algorithm with a Back Propagation neural network (BPNN) classification algorithm. Firstly, time-domain features of the bearing signals are extracted to comprehensively reflect the operational state of the bearings. Principal Component Analysis (PCA) is then utilized to reduce the dimensionality of the high-dimensional features, preserving the main information while reducing computational complexity. Subsequently, the BDA algorithm is employed to align the features of the source and target domains, balancing distribution differences and achieving effective feature space transfer. Finally, the BP neural network classification algorithm is used to classify the transferred features, thereby diagnosing bearing faults. Experimental results demonstrate that, compared to traditional fault diagnosis methods, the proposed approach achieves higher diagnostic accuracy and robustness under different working conditions. This method not only addresses the challenges posed by changing operating conditions but also holds significant practical value, providing a robust and efficient solution for real-world industrial applications such as predictive maintenance and condition monitoring in critical engineering systems. |
| format | Article |
| id | doaj-art-b30aa63c2f954682bcca7f895d71655c |
| institution | Kabale University |
| issn | 1687-8140 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Advances in Mechanical Engineering |
| spelling | doaj-art-b30aa63c2f954682bcca7f895d71655c2025-08-20T03:31:15ZengSAGE PublishingAdvances in Mechanical Engineering1687-81402025-06-011710.1177/16878132251348366Research on bearing fault feature transfer diagnosis based on balanced distribution adaptation under feature fusionLulu Wang0Yongqi Li1Chunyi Zhang2Ralph Gerard Sangalang3Guangdong University of Science and Technology, Dongguan, ChinaChina Copper Southeast Copper Co., Ltd, Ningde, Fujian, ChinaGuangdong University of Science and Technology, Dongguan, ChinaBatangas State University, PhilippinesIn practical industrial applications, the operating conditions of bearings frequently change, posing significant challenges for reliable fault diagnosis. Traditional machine learning methods, which rely on the assumption of independent and identically distributed samples, often experience a significant decline in diagnostic accuracy under such variable conditions. To address this issue, this paper proposes a bearing fault transfer diagnosis method that combines the Balanced Distribution Adaptation (BDA) algorithm with a Back Propagation neural network (BPNN) classification algorithm. Firstly, time-domain features of the bearing signals are extracted to comprehensively reflect the operational state of the bearings. Principal Component Analysis (PCA) is then utilized to reduce the dimensionality of the high-dimensional features, preserving the main information while reducing computational complexity. Subsequently, the BDA algorithm is employed to align the features of the source and target domains, balancing distribution differences and achieving effective feature space transfer. Finally, the BP neural network classification algorithm is used to classify the transferred features, thereby diagnosing bearing faults. Experimental results demonstrate that, compared to traditional fault diagnosis methods, the proposed approach achieves higher diagnostic accuracy and robustness under different working conditions. This method not only addresses the challenges posed by changing operating conditions but also holds significant practical value, providing a robust and efficient solution for real-world industrial applications such as predictive maintenance and condition monitoring in critical engineering systems.https://doi.org/10.1177/16878132251348366 |
| spellingShingle | Lulu Wang Yongqi Li Chunyi Zhang Ralph Gerard Sangalang Research on bearing fault feature transfer diagnosis based on balanced distribution adaptation under feature fusion Advances in Mechanical Engineering |
| title | Research on bearing fault feature transfer diagnosis based on balanced distribution adaptation under feature fusion |
| title_full | Research on bearing fault feature transfer diagnosis based on balanced distribution adaptation under feature fusion |
| title_fullStr | Research on bearing fault feature transfer diagnosis based on balanced distribution adaptation under feature fusion |
| title_full_unstemmed | Research on bearing fault feature transfer diagnosis based on balanced distribution adaptation under feature fusion |
| title_short | Research on bearing fault feature transfer diagnosis based on balanced distribution adaptation under feature fusion |
| title_sort | research on bearing fault feature transfer diagnosis based on balanced distribution adaptation under feature fusion |
| url | https://doi.org/10.1177/16878132251348366 |
| work_keys_str_mv | AT luluwang researchonbearingfaultfeaturetransferdiagnosisbasedonbalanceddistributionadaptationunderfeaturefusion AT yongqili researchonbearingfaultfeaturetransferdiagnosisbasedonbalanceddistributionadaptationunderfeaturefusion AT chunyizhang researchonbearingfaultfeaturetransferdiagnosisbasedonbalanceddistributionadaptationunderfeaturefusion AT ralphgerardsangalang researchonbearingfaultfeaturetransferdiagnosisbasedonbalanceddistributionadaptationunderfeaturefusion |