基于改进灰色GM(1,1)模型的轨道电路故障预测

ZPW-2000A轨道电路在保障列车安全运行过程中发挥着重要作用,一旦出现故障将造成不可预估的后果。因此,对轨道电路进行故障预测具有重要意义。本文提出改进的灰色GM(1,1)预测模型对轨道电路红光带现象进行预测分析,解决了传统灰色GM(1,1)预测模型预测精度低、存在一定误差等问题。通过引入弱化因子降低原始数据波动带来的预测误差,并应用矩形法对传统模型的背景权值进行优化,基于遗传算法求得约束条件下的最佳背景参数,得到改进的GM(1,1)预测模型。结合铁路局信号车间采集到的轨出电压数据验证改进预测模型的性能,结果表明,相比于传统灰色GM(1,1)模型,改进后的模型平均相对误差降低了28.3%,具有更高的预测精度和实用价值。

Track circuit fault prediction based on modified grey GM(1,1) model

ZPW-2000A track circuit plays an important role in the process of ensuring the safety of train operation, once the failure will cause unpredictable consequences. Therefore, fault prediction of track circuit is of great significance. In this paper, an improved grey GM(1,1) prediction model is proposed to predict and analyze the red band phenomenon of track circuit, which solves the problems of low prediction accuracy and certain error of the traditional grey GM(1,1) prediction model. By introducing the weakening factor to reduce the prediction error caused by the original data fluctuation, and using the rectangle method to optimize the background weight of the traditional model, the optimal background parameters under the constraints were obtained based on the genetic algorithm, and the improved GM(1,1) prediction model was obtained. The performance of the improved prediction model is verified by combining the rail outlet voltage data collected from the signal workshop of railway bureau. The results show that compared with the traditional grey GM(1,1) model, the average relative error of the improved model is reduced by 28.3%, and the improved model has higher prediction accuracy and practical value.