监测数据驱动的城轨列车轴箱轴承剩余寿命预测  

Monitoring Data-Driven Prediction of Remaining Useful Life of Axle-Box Bearings for Urban Rail Transit Trains

作　　者：王彪 秦勇[1] 贾利民[1] 程晓卿[1] 曾春平 高一凡 WANG Biao;QIN Yong;JIA Limin;CHENG Xiaoqing;ZENG Chunping;GAO Yifan(State Key Lab of Rail Traffic Control&Safety,Beijing Jiaotong University,Beijing 100044,China;Beijing Jinhong Xi-Dian Information Technology Co.,Ltd.,Beijing 100071,China)

机构地区：[1]北京交通大学轨道交通控制与安全国家重点实验室,北京100044 [2]北京锦鸿希电信息技术股份有限公司,北京100071

出　　处：《西南交通大学学报》2024年第1期229-238,共10页Journal of Southwest Jiaotong University

基　　金：国家自然科学基金(61833002-3);轨道交通控制与安全国家重点实验室(北京交通大学)自主研究课题(RCS2022ZQ002);北京交通大学人才基金项目(2022RC030)资助.

摘　　要：城轨列车轴箱轴承的运行工况复杂多变、外部随机干扰频繁,导致其监测数据中包含大量测量噪声乃至“脏”数据,进而制约了剩余寿命预测模型的精度.为解决上述问题,提出了一种监测数据驱动的动态多重聚合剩余寿命预测方法.首先,通过度量短时数据的幅值分布相似性自动识别并清洗“脏”数据;然后,将健康指标按不同时间尺度进行动态聚合,预测出各类潜在的未来退化轨迹,进而获得轴箱轴承的剩余寿命预测均值与方差;并使用现场实测数据与加速寿命实验数据对提出方法进行验证.结果表明:所提方法能有效剔除监测数据中的空采数据和强干扰数据;剩余寿命预测均值随累计行驶里程的增加逐渐收敛到真实值,且95%置信区间越来越窄;相比于单指数预测模型和混合预测模型,提出方法的累计相对精度平均值分别提高了29.78%和27.63%,预测收敛速度平均值分别增加了10.56%和10.20%.The operating conditions of axle-box bearings of urban rail transit trains are complex and time-varying,and they often suffer from random external interferences.Correspondingly,the monitoring data of axle-box bearings contain a great amount of measurement noise and even abnormal data,thereby limiting the accuracy of prognostics models.To overcome the aforementioned problems,a monitoring data-driven dynamic multiple aggregation prediction method is proposed for forecasting the remaining useful life(RUL)of axle-box bearings of urban rail transit trains.In the proposed method,abnormal data are first automatically recognized and deleted by measuring the amplitude distribution similarity between signals in a short time.Then,various degradation curves can be fitted to predict the mean and variance of RUL by aggregating health indicators from different temporal scales.The proposed method is evaluated using vibration data from real monitoring systems of urban rail transit trains and accelerated degradation tests of rolling element bearings.The results show that the proposed method is able to effectively recognize the not a number(NaN)data and strong interference data,and as time goes on,the predictive RUL converges to the actual RUL gradually and the 95%confidence interval becomes narrower.Further,compared with the single exponential prognostics model and the hybrid prognostics model,the proposed method increases the mean of cumulative relative accuracy by 29.78%and 27.63%respectively,and improves the mean of convergence speed by 10.56%and 10.20%respectively.

关 键 词：城轨列车 轴箱轴承 剩余寿命 数据驱动 多重聚合预测 

分 类 号：U264.8[机械工程—车辆工程]