基于面板数据的高速公路机电设备故障多因素预测模型研究

针对高速公路机电设备故障由多种因素引起的问题,对中国北京市2012-2013年6条高速公路每天的机电设备故障以及交通流量、平均温度、相对湿度和风速数据进行了调查,并以机电设备故障为被解释变量,从环境因素和工作负荷中选取交通流量、温度差、相对湿度和风速作为解释变量,分别建立混合回归、个体固定效应和随机效应面板数据模型。然后对数据序列进行单位根检验和协整检验,通过对3个预测模型采用F检验、豪斯曼检验,进行模型比较,选取最优模型。研究结果表明,个体固定效应模型较优,温度差、相对湿度和风速这些因素对机电设备故障具有显著的正效应,交通流量因素对机电设备故障具有显著的负效应;研究结果可以为高速公路机电设备的故障预测和预防性维护提供理论支持。

Multi-factor prediction model of highway electromechanical equipment faults based on panel data model

Aiming at the problems caused by many factors in the mechanical and electrical equipment failure, a panel data model was devel-oped. The electromechanical faults represented the response variable, and traffic flow, temperature difference, relative humidity, and wind speed were selected as explanatory variables. The data was based on an investigation of electromechanical equipment faults, and the above-mentioned environmental variables were collected for six highways in Beijing during 2012-2013. Panel data models for mixed regression mod-els with individual fixed and random effects were established. A unit root test and co-integration test were performed on the data sequence. Finally, theF-test and the Hausman-test were used to compare the three prediction models and determine the optimal model. The results indi-cate that the individual fixed effects model is superior, positive effects of temperature, relative humidity, and wind speed on the electrome-chanical equipment faults are significant. Traffic flow has a significant negative effect on the electromechanical equipment faults. This results can provide theoretical support for fault prediction and inform needs for preventive maintenance of the highway electromechanical equipment.