联合收割机裂纹转子与滚动轴承故障诊断系统研究OA
Research on Fault Diagnosis System of Cracked Rotor and Rolling Bearing of Combine Based Convolution Neural Network
首先,介绍了传统神经网络,在其基础上引出了改进的卷积神经网络;然后,搭建了转子和滚动轴承的动力学模型,对转子和轴承的裂纹模型进行分析研究;最后,实现了联合收割机裂纹转子与滚动轴承故障诊断系统.实验结果表明:基于卷积神经网络的诊断模型达到稳定识别精度的迭代次数更少,且识别精度更高,效果更好,证明了系统的可行性和可靠性.
It first introduces the traditional neural network,and then leads to the improved convolution neural network.Then,it built the dynamic model of the rotor and rolling bearing,and analyzed the crack model of the rotor and bearing.Finally,it realized the fault diagnosis system of the cracked rotor and rolling bearing of the combine.The experimental re-sults showed that it has less iterations to achieve stable recognition accuracy the diagnostic model based on convolution neural network.And it has higher recognition accuracy and better effect,which proves the feasibility and reliability of the system.
詹宝容;庾锡昌
广东创新科技职业学院 信息工程学院,广东 东莞 523960中国移动通信集团广东有限公司 东莞分公司,广东 东莞 523129
农业工程
联合收割机卷积神经网络转子滚动轴承裂纹故障诊断
combine harvesterconvolution neural networkrotorrolling bearingcracklefault diagnosis
《农机化研究》 2024 (005)
187-191 / 5
广东省普通高校特色创新项目(2022KTSCX384);广东创新科技职业学院校级科研项目(2022ZDYY01)
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