| 基于EMD与BP神经网络的齿轮故障诊断 |
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| 引用本文： | 刘剑生,王细洋.基于EMD与BP神经网络的齿轮故障诊断[J].失效分析与预防,2020,15(6):370-375, 392. |
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| 作者姓名： | 刘剑生 王细洋 |
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| 作者单位： | 南昌航空大学 通航学院，南昌 330063 |
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| 摘 要： | 针对基于传统BP神经网络的齿轮故障诊断方法存在收敛速度慢，误差较大等问题，提出经验模式分解（EMD）与BP神经网络相结合的齿轮故障诊断方法。首先简述经验模式分解和BP神经网络的基本原理，然后采用EMD方法提取齿轮时域信号中的各个IMF分量，计算IMF分量中故障信号能量特征参数，将这些能量特征参数作为BP神经网络输入参数进行故障诊断。在齿传动故障实验台上采集足够的样本数据进行实验研究。结果表明:与传统的BP神经网络相比，可将训练误差从0.01降低至0.001左右。此外，训练迭代次数可减小至10次以内。
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| 关 键 词： | 经验模式分解（EMD） 齿轮故障诊断 BP神经网络 齿轮 |
| 收稿时间： | 2020-08-25 |
Gear Fault Diagnosis Based on EMD and BP Neural Network |
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| LIU Jian-sheng,WANG Xi-yang.Gear Fault Diagnosis Based on EMD and BP Neural Network[J].Failure Analysis and Prevention,2020,15(6):370-375, 392. |
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| Authors: | LIU Jian-sheng WANG Xi-yang |
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| Abstract: | Aiming at the problems of slow convergence speed and large error in gear fault diagnosis based on traditional BP neural network, a gear fault diagnosis method combining EMD and BP neural network is proposed. Firstly, the basic principles of EMD and BP neural network are introduced. Then, the IMF components of gear time domain signals are extracted by EMD method , the energy characteristic parameters of fault signals in IMF components are calculated, and these energy characteristic parameters are used as input parameters of BP neural network for fault diagnosis. Sufficient sample data are collected on the gear transmission failure test-bed for experimental study. The results show that compared with the traditional BP neural network, the training error can be reduced from 0.01 to 0.001. In addition, the number of training iterations can be reduced to less than 10. |
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