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| Fault Diagnosis of High-speed Train Yaw Dampers Based on One-dimensional Residual Convolutional Attention |
| CHEN Guang1,2,SUN Zeming1,2,MA Wenda1,2,ZHANG Wan3 |
1. Changzhou Langruikaierbi Damping Technology Co., Ltd, Changzhou, Jiangsu 213125, China
2. CRRC Qishuyan Institute Co.,Ltd, Changzhou, Jiangsu 213011, China
3. Department of Automation,Nanjing University of Information Science and Technology, Nanjing, Jiangsu 210044, China |
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Abstract To address the challenge of difficulty in fault identification caused by manually extracting fault characteristics from high-speed train dampeners, a fault diagnosis algorithm based on one-dimensional residual convolutional attention (1DRCA) is proposed. This algorithm is designed to recognize the four states in the yaw damper of high-speed trains. Firstly, a convolutional layer is constructed for feature extraction, utilizing the convolutional block attention module to adaptively optimize features in both channel and spatial dimensions. Additionally, a residual neural network model is established, adjusting weight parameters using residual information. The experimental validation demonstrates the feasibility of this method for recognizing the four states of the yaw damper, accurately identifying normal, poor startup, symmetry rate faults, and sawtooth faults, with the proposed method achieving an average accuracy rate of approximately 99%. To further substantiate the model's generalization capability, fault data from rolling bearings is employed to validate the effectiveness and accuracy of the proposed model. The results indicate that the proposed model effectively diagnoses different fault states in rolling bearings.
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Received: 30 October 2023
Published: 04 July 2024
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2024, Vol. 45 
