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
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.
HUANG C H, ZENG J, SONG C Y. Simplified physical parameter model of high-speed train yaw damper［J］. Journal of the China Railway Society, 2021, 43(7): 47-56.
YAO Y, CHENG J, ZHANG M Y, et al. Mechanism analysis of yaw damper in high-speed train and frequency-dependent stiffness application［J］. Journal of Vibration Engineering, 2022: 35(6): 1461-1470.
CENG C Y, DAI L C, CHI M R, et al. Yaw damper fault diagnosis based on refined composite multiscale dispersion entropy［J/OL］. Journal of Railway Science and Engineering: 1-11. DOI: 10.19713/j.cnki.43-1423/u. T20232079.
QIN N, JIN W D, HUANG J, et al. Feature extraction of high speed train bogie based on ensemble empirical mode decomposition and sample entropy［J］. Journal of Southwest Jiaotong University, 2014, 49(1): 27-32.
HOU D X, ZHOU Z A, CHENG R C, et al. Based on the GADF-TL-ResNeXt rolling bearing fault diagnosis method［J］. Acta Methrologica Sinica, 2023, 44(10): 1534-1542.
ZHENG H, LI G, LI D C. Fault diagnosis of anti-yaw damper based on PELCD sample entropy［J］. Electric Locomotives & Mass Transit Vehicles, 2023, 46(06): 34-41.
HOU W Q, YE M, LI W H. Rolling element bearing fault classification using improved stacked de-noising auto-encoders［J］. Journal of Mechanical Engineering, 2018, 54(7): 87-96.
ZHOU X K, YU J B. Gearbox Fault Diagnosis Based on One-dimension residual convolutional auto-encoder［J］. Journal of Mechanical Engineering, 2020, 56(7): 96-108.
［17］
WANG H, LIU Z, PENG D, et al. Understanding and learning discriminant features based on multiattention 1DCNN for wheelset bearing fault diagnosis［J］. IEEE Transactions on Industrial Informatics, 2019, 16(9): 5735-5745.
［7］
ZHANG W, JIA M, ZHU L, et al. Comprehensive overview on computational intelligence techniques for machinery condition monitoring and fault diagnosis［J］. Chinese Journal of Mechanical Engineering, 2017, 30(4): 782-795.
CHEN B J, CHEN X L, SHEN B M, et al. An application of convolution neural network and long short-term memory in rolling bearing fault diagnosis［J］. Journal of Southwest Jiaotong University, 2021, 55(6): 28-36.
JIN H L, MA W X, MENG Z, et al. An improved 1DCNN-GRU for rolling bearing fault diagnosis［J］. Acta Metrologica Sinica, 2023, 44(9): 1423-1428.
JIA M X, HAN B D, WANG J R, et al. Bearing fault diagnosis method based on transfer learning and stacked auto-encoders［J］. Noise and Vibration Control, 2021, 41(6): 84-89.
CHEN G, MA W D, SUN Z M, et al. Fault diagnosis of yaw damper in high-speed train based on convolutional neural network［J］. Machine Tool & Hydraulics, 2023, 51(8): 194-199.
［16］
ZHANG W, LI C H, PENG G L, et al. A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load［J］. Mechanical systems and signal processing, 2018, 100: 439-453.
2024, Vol. 45 
