基于核函数的寿命预测融合算法研究  

Research on fusion algorithm for service life prediction based on kernel functions

作　　者：唐欢 李依竹 吕鹏 TANG Huan;LI Yizhu;LYU Peng(CRRC Zhuzhou Institute Co.,Ltd.,Zhuzhou,Hunan 412001,China)

机构地区：[1]中车株洲电力机车研究所有限公司,湖南株洲412001

出　　处：《机车电传动》2024年第5期12-16,共5页Electric Drive for Locomotives

摘　　要：实时寿命预测是评估运营中的轨道交通控制装置剩余寿命的有效手段,目前实时寿命预测主要基于故障物理和基于退化数据这2种方法。在轨道交通控制装置生命周期的早期,由于缺乏实时采集的退化数据,基于故障物理方法的预测结果会优于基于退化数据方法,而在生命周期末期,随着大量退化数据的积累,基于退化数据方法的预测结果会更加精确。因此,文章提出了一种基于核函数的寿命预测融合算法,将故障物理方法与退化数据方法的预测结果进行融合,以获取全生命周期更为准确的寿命预测结果。文章以轨道交通控制装置某核心板卡上的光耦为例,通过加速退化试验的数据对比验证了故障物理方法、退化数据方法和融合算法的结果,结果显示融合算法在全生命周期内具有更高的预测精度。Real-time service life prediction offers an effective means of assessing the remaining service life of operational control devices in rail transit systems.Currently,the primary methods employed for this purpose are based on physics-of-failure analysis and degradation data,respectively.Early in the lifecycle of these control devices,the lack of real-time degradation data makes the physics-of-failure-based methods more accurate.However,as these devices approach the end of their lifecycle,a large amount of degradation data accumulates,allowing the degradation-data-based methods to yield more precise predictions.This paper proposes a kernel function-based fusion algorithm for service life prediction that combines predictions from both methods to enhance accuracy in predictions throughout the entire lifecycle.Accelerated degradation experiments were conducted using an optical coupler on a core board of a rail transit control device to compare the results from the physics-of-failure-based method,the degradation-data-based method,and the pro-posed fusion algorithm.The results demonstrate that the fusion algorithm achieves higher prediction accuracy throughout the entire life-cycle compared with the other two methods.

关 键 词：城市轨道交通 实时寿命预测 故障物理 退化数据 核函数 

分 类 号：U239.9[交通运输工程—道路与铁道工程]