Abstract：Because of its simple and reliable connection, as well as the advantages of space saving and weight reduction, the wedge-shaped ring connection structure is often used in torpedoes, aerospace vehicles and other weapons and equipment. Aiming at the problems of complex mechanism model, small sample size and class imbalance of wedge-shaped ring connection structure, a method of Pre-tightening state identification based on the siamese neural network is proposed in this paper. A time-frequency feature enhancement technology is used to improve the efficiency and effect of model training. Based on the enhanced features, a three-layer siamese neural network classification model is established to realize the macroscopic classification of Pre-tightening state identification of wedge-ring connection structure. On this basis, in order to guide the precision assembly, the feature clustering effect of siamese neural network is deeply analyzed by feature visualization technology. The pre-tightening state quantitative characterization model is established by using two-dimensional features, and the target state clustering center and acceptance domain are introduced to realize the quantitative evaluation of the pre-tightening state of the wedge-ring connection structure. Experiment has verified the effectiveness of the proposed method. The research results provide a new technical approach and solution for Pre-tightening state identification of wedge-ring connection structure.
盛俊杰，王九龙，李树勇，文勇. 基于孪生神经网络的楔形环连接结构预紧状态辨识[J]. 振动与冲击, 2024, 43(8): 162-168.
SHENG Junjie,WANG Jiulong,LI Shuyong,WEN Yong. Pre-tightening state identification of a wedge-ring connection structure based on thesiamese neural network. JOURNAL OF VIBRATION AND SHOCK, 2024, 43(8): 162-168.
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