摘要
在小波神经网络算法的基础上,从时域和频域两方面对汽车齿轮箱的振动信号进行分析并提取时频域的多个表征值,设计了一种应用于汽车齿轮箱故障诊断的BP神经网络算法。采用经验模态分解法对齿轮箱时频域下的多维故障特征值进行分析和提取,导出了BP神经网络算法步骤和诊断模型;进一步以JZQ! 250齿轮箱为研究对象,对该算法进行数据训练和验证,其状态实验数据结果表明,该算法能够在考虑汽车齿轮箱复杂故障下实现正确诊断,其用于汽车变速箱故障诊断具有较好的实用性,对汽车齿轮箱的故障诊断提供了一定借鉴。
Based on the wavelet neural network algorithm,the vibration signals of the automobile gearbox is analyzed from the time domain and the frequency domain,and the values with time domain and frequency domain characterization are extracted,and a method of BP neural network algorithm for the fault diagnosis of the automobile gearbox is designed.The fault of the gearbox is analyzed and extracted by empirical mode decomposition method in of the time-frequency domain.In addition,the method of BP neural network algorithm and the diagnosis models is erected.The JZQ-250 gearbox is used to train the algorithm of neural network algorithm to meet the needs of fault diagnose.The experimental results show that the BP neural network algorithm can reflect the correction of diagnosis under complicated fault of the automobile gearbox,which is of great practicality for fault diagnosis of the automobile gearbox.An idea and some experience for vehicle gearbox fault diagnosis in practice are provided.
作者
杨家印
Yang Jiayin(Xuzhou Economics and Trading Branch,Jiangsu Union Technical Institute,Xuzhou 221004,China)
出处
《机械传动》
北大核心
2019年第1期150-153,共4页
Journal of Mechanical Transmission
关键词
汽车齿轮箱
时频特征
BP神经网络
故障诊断
Vehicle gearbox
Time-frequency character
BP neural network
Fault diagnosis
