基于PSO_RF双向特征选择和LightGBM设备故障检测  

Device Fault Detection Based on PSO_RF Bidirectional Feature Selection and LightGBM

作　　者：韩金鹏 李冬梅 王嵩[2] HAN Jin-Peng;LI Dong-Mei;WANG Song((University of Chinese Academy of Sciences,Beijing 100049,China;Shenyang Institute of Computing Technology,Chinese Academy of Sciences,Shenyang 110168,China)

机构地区：[1]中国科学院大学,北京100049 [2]中国科学院沈阳计算技术研究所,沈阳110168

出　　处：《计算机系统应用》2020年第7期228-232,共5页Computer Systems & Applications

摘　　要：仪器共享平台的发展提高了各高校的仪器设备的使用率.但是在设备的使用过程中,对设备的故障检测方面还没有得到改善.针对上述问题,本文收集了医用影像设备的相关数据,采用PSO_RF的双向特征选择方法进行特征选择,然后构建了基于LightGBM(Light Gradient Boosting Machine)的故障检测模型,并将其应用于医用影像设备的故障检测中.通过标准评价体系的建立及不同模型对故障诊断结果的对比,相对于传统的机器学习算法,该模型在故障检测的精确率、召回率、F1值等评价指标上有较好的表现,对于加快仪器故障点的发现以及提高仪器利用率具有积极推进作用.The development of the instrument sharing platform has increased the utilization rate of instruments and equipment in various universities.However,during the use of the equipment,the fault detection of the equipment has not been improved.In view of the above problems,this study collected relevant data of medical imaging equipment,adopted the two-way feature selection method of PSO_RF for feature selection,then built a fault detection model based on LightGBM(Light Gradient Boosting Machine),and applied it to the fault detection of medical imaging equipment.Through the establishment of the standard evaluation system and the comparison of fault diagnosis results by different models,compared with the traditional machine learning algorithm,this model has a better performance in the accuracy rate,recall rate,F1 value and other evaluation indicators of fault detection,which has a positive role in accelerating the discovery of instrument fault points and improving the utilization rate of instruments.

关 键 词：设备故障检测 特征选择 LightGBM 

分 类 号：TP277[自动化与计算机技术—检测技术与自动化装置] TH776[自动化与计算机技术—控制科学与工程]