S.

Haiyang Wu1, Hui Zhou2, Chang Liu3

  • 1School of Mechanical and Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, China.

PubMed
概括

本研究介绍了一种改进的S转换和深度可分离卷积神经网络 (DSCNN),用于诊断剪刀臂轮故障. 该方法增强了时间频率分析,并且在减少训练时间和参数的情况下实现了高精度.

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