TY -的A2 Forcina安东尼奥AU -叮,Jiakai AU -肖,东明AU -黄,Xuejun PY Liangpei AU - Li - 2020 DA - 2020/12/18 TI -齿轮故障诊断基于VMD离散Hopfield神经网络样本熵和SP - 8882653六世- 2020 AB -齿轮故障信号的非平稳非线性等有一些缺陷。为了提高齿轮的使用寿命,设备操作监控。基于变分的齿轮故障诊断方法模式分解(VMD)样本熵和离散Hopfield神经网络(DHNN)提出。首先,选择的最优VMD分解号码是瞬时频率平均值。然后,样本熵值提取每个固有模态函数(IMF),形成齿轮特征向量。齿轮特征向量是编码和用作内存DHNN原型和记忆的起点,分别。最后,输入编码向量DHNN实现故障模式识别。新定义的编码规则产生重大影响齿轮故障诊断的准确性。由self-associative内存驱动,齿轮故障的编码由DHNN准确分类。齿轮故障诊断的优越性VMD-DHNN方法验证通过比较先进的信号处理算法。 The results show that the accuracy based on VMD sample entropy and DHNN is 91.67% of the gear fault diagnosis method. The experimental results show that the VMD method is better than the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and empirical mode decomposition (EMD), and the effect of it in the diagnosis of gear fault diagnosis is emphasized. SN - 1024-123X UR - https://doi.org/10.1155/2020/8882653 DO - 10.1155/2020/8882653 JF - Mathematical Problems in Engineering PB - Hindawi KW - ER -