摘要
为了避免制丝生产线设备故障造成的停机时间和生产损失,快速定位和识别制丝生产线设备振动故障,提出了基于物联网和模糊推理的制丝生产线设备振动故障数据挖掘优化模型。应用物联网技术,结合传感器收集制丝生产线设备故障样本,并在预处理初筛阶段去除冗余的设备信息;利用正交分解算法引入特征标记函数,分析振动数据属性,完成振动故障数据特征提取和分类;凭借模糊推理算法构建数据挖掘优化模型,利用非线性函数优化改进模糊推理过程,挖掘振动故障数据。经实验证明,所提方法能有效地完成振动故障数据挖掘,并分辨具体的故障位置,振动幅值主要集中在-5 mm/s~25 mm/s,具有很好的可应用性。
In order to avoid downtime and production losses caused by equipment failures in the silk production line,and to quickly locate and identify vibration faults in silk production line equipment,a data mining optimization model for vibration faults in silk production line equipment based on the Internet of Things and fuzzy reasoning is proposed.Internet of Things technology,combined with sensors,is applied to collect fault samples of silk production line equipment,and redundant equipment information is removed in the preliminary screening stage of preprocessing.Orthogonal decomposition algorithm is used to introduce feature labeling function,vibration data attributes are analyzed,and vibration fault data feature extraction and classificationare completed.A data mining optimization model is built by using fuzzy inference algorithms,the fuzzy inference process is improved by using nonlinear function optimization,and vibration fault data are mined.Experimental results have shown that the proposed method can effectively mine vibration fault data and distinguish specific fault lo-cations.The vibration amplitude is mainly concentrated in the range of-5 mm/s~25 mm/s,which has good applicability.
作者
韦小玲
唐芳丽
梁志远
李俊超
张野
王煜轩
刘运起
WEI Xiaoling;TANG Fangli;LIANG Zhiyuan;LI Junchao;ZHANG Ye;WANG Yuxuan;LIU Yunqi(Liuzhou Cigarette Factory,Guangxi China Tobacco Industry Co.,Ltd.,Liuzhou Guangxi 545001,China;National Machinery Industry Internet Research Institute(Henan)Co.,Ltd.,Zhengzhou Henan 450007,China)
出处
《电子器件》
2025年第4期955-960,共6页
Chinese Journal of Electron Devices
关键词
数据挖掘
物联网
模糊推理
制丝生产线设备
振动数据
挖掘模型
data mining
Internet of Things
fuzzy reasoning
silk making equipment
vibration data
mining models
