Gearbox Fault Diagnosis Under Noise and Variable Operating Conditions Using Multiscale Depthwise Separable Convolution and Bidirectional Gated Recurrent Unit with a Squeeze-and-Excitation Attention Mechanism
Gearbox condition monitoring is essential for ensuring the reliability of power transmission systems. However, the existing methods are constrained by shallow feature extraction and unidirectional temporal modeling. To address these limitations, this study proposes a novel fault diagnosis framework...
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MDPI AG
2025-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/10/2978 |
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| author | Xiaoteng Ma Kejia Zhai Nana Luo Yehui Zhao Guangming Wang |
| author_facet | Xiaoteng Ma Kejia Zhai Nana Luo Yehui Zhao Guangming Wang |
| author_sort | Xiaoteng Ma |
| collection | DOAJ |
| description | Gearbox condition monitoring is essential for ensuring the reliability of power transmission systems. However, the existing methods are constrained by shallow feature extraction and unidirectional temporal modeling. To address these limitations, this study proposes a novel fault diagnosis framework that integrates multiscale depthwise separable convolution, bidirectional gated recurrent units, and a squeeze-and-excitation attention mechanism. This approach enables multiscale feature extraction from vibration signals, bidirectional temporal modeling, and the enhancement of critical fault-related information. The experimental results demonstrate that the proposed method significantly outperforms conventional models in terms of fault diagnosis accuracy, noise robustness, and adaptability to varying operating conditions. The attention mechanism effectively suppresses noise interference, while bidirectional temporal modeling accurately captures fault propagation characteristics, thereby improving adaptability to dynamic conditions. This research provides a highly robust solution for intelligent gearbox fault diagnosis in complex industrial environments. |
| format | Article |
| id | doaj-art-2cf2d66db84b446399677dea7ba742ba |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-2cf2d66db84b446399677dea7ba742ba2025-08-20T01:56:39ZengMDPI AGSensors1424-82202025-05-012510297810.3390/s25102978Gearbox Fault Diagnosis Under Noise and Variable Operating Conditions Using Multiscale Depthwise Separable Convolution and Bidirectional Gated Recurrent Unit with a Squeeze-and-Excitation Attention MechanismXiaoteng Ma0Kejia Zhai1Nana Luo2Yehui Zhao3Guangming Wang4College of Information Science and Engineering, Shandong Agricultural University, Taian 271018, ChinaCollege of Information Science and Engineering, Shandong Agricultural University, Taian 271018, ChinaCollege of Mechanical and Electronic Engineering, Shandong Agricultural University, Taian 271018, ChinaCollege of Engineering, Ocean University of China, Qingdao 266404, ChinaCollege of Mechanical and Electronic Engineering, Shandong Agricultural University, Taian 271018, ChinaGearbox condition monitoring is essential for ensuring the reliability of power transmission systems. However, the existing methods are constrained by shallow feature extraction and unidirectional temporal modeling. To address these limitations, this study proposes a novel fault diagnosis framework that integrates multiscale depthwise separable convolution, bidirectional gated recurrent units, and a squeeze-and-excitation attention mechanism. This approach enables multiscale feature extraction from vibration signals, bidirectional temporal modeling, and the enhancement of critical fault-related information. The experimental results demonstrate that the proposed method significantly outperforms conventional models in terms of fault diagnosis accuracy, noise robustness, and adaptability to varying operating conditions. The attention mechanism effectively suppresses noise interference, while bidirectional temporal modeling accurately captures fault propagation characteristics, thereby improving adaptability to dynamic conditions. This research provides a highly robust solution for intelligent gearbox fault diagnosis in complex industrial environments.https://www.mdpi.com/1424-8220/25/10/2978gearboxfault diagnosisconvolutional neural networkgated recurrent unitattention mechanism |
| spellingShingle | Xiaoteng Ma Kejia Zhai Nana Luo Yehui Zhao Guangming Wang Gearbox Fault Diagnosis Under Noise and Variable Operating Conditions Using Multiscale Depthwise Separable Convolution and Bidirectional Gated Recurrent Unit with a Squeeze-and-Excitation Attention Mechanism Sensors gearbox fault diagnosis convolutional neural network gated recurrent unit attention mechanism |
| title | Gearbox Fault Diagnosis Under Noise and Variable Operating Conditions Using Multiscale Depthwise Separable Convolution and Bidirectional Gated Recurrent Unit with a Squeeze-and-Excitation Attention Mechanism |
| title_full | Gearbox Fault Diagnosis Under Noise and Variable Operating Conditions Using Multiscale Depthwise Separable Convolution and Bidirectional Gated Recurrent Unit with a Squeeze-and-Excitation Attention Mechanism |
| title_fullStr | Gearbox Fault Diagnosis Under Noise and Variable Operating Conditions Using Multiscale Depthwise Separable Convolution and Bidirectional Gated Recurrent Unit with a Squeeze-and-Excitation Attention Mechanism |
| title_full_unstemmed | Gearbox Fault Diagnosis Under Noise and Variable Operating Conditions Using Multiscale Depthwise Separable Convolution and Bidirectional Gated Recurrent Unit with a Squeeze-and-Excitation Attention Mechanism |
| title_short | Gearbox Fault Diagnosis Under Noise and Variable Operating Conditions Using Multiscale Depthwise Separable Convolution and Bidirectional Gated Recurrent Unit with a Squeeze-and-Excitation Attention Mechanism |
| title_sort | gearbox fault diagnosis under noise and variable operating conditions using multiscale depthwise separable convolution and bidirectional gated recurrent unit with a squeeze and excitation attention mechanism |
| topic | gearbox fault diagnosis convolutional neural network gated recurrent unit attention mechanism |
| url | https://www.mdpi.com/1424-8220/25/10/2978 |
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