1.College of Mechanical and Automotive Engineering, Liaocheng University, Liaocheng 252000,China；
2. College of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266520, China;
3. Dezhou Environmental Health Service Center, Dezhou 253017, China;
4. College of Intelligent Manufacturing, Qingdao Huanghai University, Qingdao 266520, China;
5. College of Weapons Engineering, Naval Engineering University, Wuhan 430032, China
Abstract：To address the current problems of quantitative testing of broken wires for steel wire ropes, using the powerful feature extraction ability of deep convolution neural network, a quantitative identification method based on transfer learning is proposed. The original magnetic flux leakage signals are converted into time-frequency images by continuous wavelet transform. The low-level parameters of the pre-trained network GoogLeNet are directly transferred, and the labeled time-frequency images are used to adjust the parameters in the high level of the network to obtain the final target model. The effectiveness of the proposed quantitative recognition model is verified by the experiments of internal and external broken wires. The traditional BP(back propagation) neural network is used as a comparison. The results show that the quantitative identification method based on transfer learning can accurately classify the internal and external broken wires of the wire rope with an accuracy rate of 97.2%. Compared with the traditional BP neural network, the proposed method has better recognition performance for various broken wires.
张义清1，谭继文2，孟庆文3，曾实现4，白晓瑞5. 基于迁移学习的钢丝绳断丝定量检测方法[J]. 振动与冲击, 2022, 41(12): 261-266.
ZHANG Yiqing1，TAN Jiwen2，MENG Qingwen3，ZENG Shixian4，BAI Xiaorui5. A quantitative testing method for broken wires in steel wire ropes based on transfer learning. JOURNAL OF VIBRATION AND SHOCK, 2022, 41(12): 261-266.
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