基于小波变换的病理组织包埋机故障检修方法研究  

Research on troubleshooting method of pathological tissueembedding machine based on wavelet transform

作　　者：王喆[1] 李利 WANG Zhe;LI Li(Shanxi Eneergy Lnstitute,Xianyang Shanxi 712000,China;Hanzhong Railway Central Hospital,Hanzhong Shanxi 723000,China)

机构地区：[1]陕西能源职业技术学院,陕西咸阳712000 [2]汉中市铁路中心医院,陕西汉中723000

出　　处：《自动化与仪器仪表》2021年第6期182-185,共4页Automation & Instrumentation

基　　金：2020年度校级教育教学改革研究课题:《医学影像成像原理》课程思政教育的探究与实践(No.20XJY10)。

摘　　要：为了提高病理组织包埋机故障检修能力,提出基于小波变换的病理组织包埋机故障检修方法。构建病理组织包埋机故障信息采集模型,采集的病理组织包埋机故障数据主要有机电装置传感数据、振动数据、输出稳压数据和输出电流振荡数据等。分析病理组织包埋机故障特征提取模型,结合谱特征融合检测方法进行病理组织包埋机故障参数融合处理,构建病理组织包埋机故障诊断的模糊度特征辨识模型,通过空间参数寻优方法进行病理组织包埋机故障类别辨识,采用多尺度小波变换方法进行病理组织包埋机故障特征量的谱分解,根据特征聚类结果实现病理组织包埋机故障诊断和检修。仿真结果表明,采用该方法进行病理组织包埋机故障检测的自适应性较好,特征辨识水平较高,提高了病理组织包埋机故障诊断能力。In order to improve the troubleshooting ability of pathological tissue embedding machine,a troubleshooting method of pathological tissue embedding machine based on wavelet transform is proposed.The failure information collection model of pathological tissue embedding machine is constructed.The collected failure data of pathological tissue embedding machine mainly include electromechanical device sensing data,vibration data,output voltage stabilization data and output current oscillation data.The fault feature extraction model of pathological tissue embedding machine is analyzed,and the fault parameters of pathological tissue embedding machine are fused with spectral feature fusion detection method.The fuzzy feature identification model of pathological tissue embedding machine fault diagnosis is constructed.The fault classification of pathological tissue embedding machine is identified by spatial parameter optimization method,and the spectral decomposition of fault feature quantity of pathological tissue embedding machine is carried out by multi-scale wavelet transform method.The fault diagnosis and maintenance of pathological tissue embedding machine are realized according to feature clustering results.The simulation results show that this method has good adaptability and high level of feature recognition,which improves the fault diagnosis ability of pathological tissue embedding machine.

关 键 词：小波变换 病理组织包埋机 故障 检修 谱特征融合 

分 类 号：TH165.3[机械工程—机械制造及自动化]