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作 者:王建斌 Wang Jianbin(Shanxi Xinzhou Shenda Daqiaogou Coal Industry Co.,Ltd.,Xinzhou Shanxi 036500,China)
机构地区:[1]山西忻州神达大桥沟煤业有限公司,山西忻州036500
出 处:《机械管理开发》2024年第10期103-105,共3页Mechanical Management and Development
摘 要:为探究齿轮箱故障识别分析准确率的提升路径,结合某型采煤机的行星齿轮箱为案例进行研究,首先通过虚拟样机技术搭建仿真模型,为故障信息提供基础数据;而后以卷积神经网络(CNN)为基础,搭建用于齿轮箱故障模式识别分析的神经网络系统;最后对该系统的性能进行测试。测试结果显示,该系统在故障识别分析准确率方面更具优势,证明其具有潜在应用价值。In order to explore the improvement path of the accuracy of gearbox fault recognition analysis,combined with a certain type of coal mining machine planetary gearbox as a case study,first of all,through the virtual prototype technology to build a simulation model,to provide basic data for the fault information;and then based on the convolutional neural network(CNN),to build a neural network system for the gearbox fault pattern recognition analysis;and finally,to test the performance of the system.Finally,the performance of the system is tested.The test results show that the system is more advantageous in terms of the accuracy of fault recognition and analysis,which proves that it has potential application value.
分 类 号:TM315[电气工程—电机] TP183[自动化与计算机技术—控制理论与控制工程]
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