基于支持向量回归的地铁受电弓滑板磨耗趋势预测模型研究

针对列车供电系统中重要组成部分之一的受电弓滑板磨耗问题，设计了一款预测模型对地铁受电弓滑板磨耗趋势进行有效的拟合和预测，弥补了现有的检测系统只能对受电弓进行实时检测的不足。利用线性支持向量回归（SVR-Linear）、最小二乘支持向量回归（LSSVR）和优化后的最小二乘支持向量回归（MI-LSSVR）对检测系统得到的受电弓滑板数据进行训练和拟合，并利用训练后的模型实现滑板磨耗的预测，其中，MI-LSSVR的拟合精度最高，达到97.3%。此外，利用地铁行走的里程数据进行预测，提前得到下一次运行后的滑板厚度，在滑板即将磨耗到限时进行预测，可得到滑板还能承受的运行里程，减少受电弓检修人员的工作量，提高受电弓的使用效率。

Prediction model of subway pantograph slide pan wear trend based on LSSVR

This paper designed a prediction model to effectively fit and predict the pantograph slide pan wear trend, which made up for the deficiency that the existing detection system could only detect the pantograph in real time.The paper used the linear support vector regression(SVR linear), least square support vector regression(LSSVR) and optimized least square support vector regression(MI-LSSVR) to train and fit the pantograph slide pan data obtained by the detection system, and used the model after training to predict the wear of the slide pan. The fitting accuracy of MILSSVR could reach 97.3%.In addition, the model could also be used to predict the thickness of the slide pan after the next operation in advance by using the mileage data of subway. When the slide pan was about to wear to the limit, the model could be used to predict the operating mileage that the slide pan could bear,reduce the workload of pantograph maintenance personnel, and improve the use efficiency of pantograph.