摘要
金属化薄膜电容器是轨道交通变流系统的重要元器件之一,精确预测其剩余寿命有助于优化变流系统的运行维护策略,降低系统运用成本,同时降低故障和突发事故发生所带来的损失。由于老化试验成本与时间的限制,当前的寿命预测存在数据样本数小、特征参数测量点少等问题,因此,文中采用能较好适应该类样本的支持向量回归(support vector regression,SVR),并利用深度置信网络(deep belief network,DBN)在未构建复杂且未知的经验退化函数下对电容量退化序列的特征进行深度提取,建立融合DBN与“双通道”支持向量回归(binary support vector regression,BSVR)的预测模型,其中“第一通道”基于Linear核函数的SVR对序列实行多步递归预测(recursive multi-step forecast strategy,RMFS),随后将所获误差代入基于Gauss核函数的“第二通道”SVR完成误差补偿训练,较好地消除了实行RMFS时所造成的累积误差。就此,提出基于特征提取与误差补偿的剩余寿命预测方法,并在老化试验下的金属化薄膜电容器电容量退化数据集上对所提方法进行仿真实验,实验结果表明,该方法在不同预测起点的平均预测误差仅为7.08%,相比已有预测方法有效提升了对电容器剩余寿命的预测精度,增强了模型的可靠性和泛化能力。
Metallized film capacitor(MFC) plays a great role in the traction system of rail transit. The accurate prediction of its lifetime is helpful to optimize the operation and maintenance strategy of the converter system, and reduce the cost of system operation and the losses caused by failures and accidents. Aging test requires a lot of cost and time, which leads to the problems of small number of data samples and few parameter measurement points in current lifetime prediction.Hence, this paper took the advantages of Support Vector Regression(SVR) that could be suitable for these samples and implements Deep Belief Network(DBN) to in-depth extract features from time series without sophisticated and unknown empirical degradation function. A model which combines DBN and Binary SVR(BSVR) was built, where one with linear kernel function performs Recursive Multi-step Forecast Strategy(RMFS) on degradation series and another based on the Gauss kernel function completes the error compensation training after obtaining the error, which eliminates accumulated error of RMFS. Thereupon, a residual lifetime prediction method based on deep feature extraction and error compensation was simulated on the dataset of capacitance degradation of MFC under aging test. The experimental results showed the average prediction error at different prediction starting points was only 7.08%. Compared with existing methods, the proposed method greatly heightens the residual lifetime prediction accuracy of MFC and enhances the reliability as well as generalization ability.
作者
成庶
刘嘉文
伍珣
于天剑
向超群
袁东辉
CHENG Shu;LIU Jiawen;WU Xun;YU Tianjian;XIANG Chaoqun;YUAN Donghui(School of Traffic and Transportation Engineering,Central South University,Changsha 410075,Hunan Province,China;CRRC Changchun Railway Vehicles Co.,Ltd.,Changchun 130062,Jilin Province,China)
出处
《中国电机工程学报》
EI
CSCD
北大核心
2022年第7期2672-2680,共9页
Proceedings of the CSEE
基金
国家自然科学基金项目(52072414)。
关键词
金属化薄膜电容器
特征提取
误差补偿
剩余寿命预测
metallized film capacitor
feature extraction
error compensation
residual lifetime prediction
