摘要
首先,介绍了小波变换原理及其在故障检测中的优势;然后,采用三层小波神经网络模型,构造了旋耕装置机械故障诊断模型;最后,从建立旋耕装置故障特征、提取故障特征向量,以及建立和训练小波神经网络模型等方面,实现了基于小波神经网络的旋耕装置机械故障诊断模型,能够实时完成对旋耕装置的机械故障诊断。通过验证与分析,证明了诊断系统的可行性和精确性。
It first introduces the principle of wavelet transform and its advantages in fault detection,and then uses three layers of wavelet neural network model to construct the mechanical fault diagnosis model of the rotary tillage device.Finally,it established the fault characteristics of the rotary tillage device,the extraction of the fault feature vector,and the establishment and training of the wavelet neural network model.It realized the mechanical fault diagnosis model of rotary tillage device based on wavelet neural network,whcih can complete the mechanical fault diagnosis of rotary tiller in real time.The results of validation and analysis show the feasibility and accuracy of the diagnostic system.
作者
郭庆军
Guo Qingjun(Chongqing Jianzhu College,Chongqing 400072,China)
出处
《农机化研究》
北大核心
2019年第9期194-198,232,共6页
Journal of Agricultural Mechanization Research
基金
重庆市教育委员会重点科技攻关项目(172102310335)
关键词
小波变换
小波神经网络
旋耕装置
故障诊断
wavelet transform
wavelet neural network
rotary tiller
fault diagnosis
