基于数据挖掘技术的混凝土泵车摆缸油温预测研究  

Research on Concrete Pump Truck Swing Cylinder Oil Temperature Prediction Based on Data Mining Technology

作　　者：程一凡[1] 段鹏辉 岳文辉 CHENG Yi-fan;DUAN Peng-hui;YUE Wen-hui(Hunan Electrical College of Technology,Xiangtan 411101,China;Xiangtan Electric Manufacturing Group Limited,Xiangtan 411101,China;Hunan Provincial Key Laboratory of Health Maintenance for Mechanical Equipment,Hunan University of Science and Technology, Xiangtan 411201,China)

机构地区：[1]湖南电气职业技术学院,湘潭411101 [2]湘电集团,湘潭411101 [3]湖南科技大学机械设备健康维护湖南省重点实验室,湘潭411201

出　　处：《内燃机与配件》2018年第20期15-19,共5页Internal Combustion Engine & Parts

摘　　要：混凝土泵车在服役过程中的运行状态和维护信息是产品面向再制造设计、再制造零部件寿命预测和保障再制造产品质量的基础。通过数据挖掘技术对ECC系统中的海量数据进行分析,并基于线性回归分析、神经网络方法建立了摆缸液压油温度的预测模型。比较两种模型的预测精度,结果表明摆缸液压油温度可由其他参数利用线性回归预测模型获得。这有利于在面向再制造设计中减少一个温度传感器、ECC系统所监测的数据量及其存储负担,从而减少系统的复杂性,因而具有一定的实际应用价值。The information of running status and maintenance in the service process of concrete pump truck is the basis of products for remanufacturing design,remanufacturing components life prediction and guarantee for the quality of remanufacturing product.The data mining technology is used to analyze the massive data in ECC system,and the prediction model of swing cylinder hydraulic oil temperature is established based on both linear regression analysis and neural network method.Comparing the prediction accuracy of the two kinds of model,the result shows that swing cylinder hydraulic oil temperature can be obtained by other parameters that using linear regression prediction model,which can reduce the amount of data and storage burden of a temperature sensor and ECC system monitoring in the remanufacturing design,so as to reduce the complexity of system,thus it is with a certain applied value.

关 键 词：数据挖掘 混凝土泵车 油温 预测模型 再制造 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]