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基于ARMA模型的工业电容退化状态研究方法
引用本文:张峥,陶耀东,余骋远.基于ARMA模型的工业电容退化状态研究方法[J].计算机系统应用,2017,26(7):30-35.
作者姓名:张峥  陶耀东  余骋远
作者单位:中国科学院大学, 北京 100049,中国科学院大学, 北京 100049;中国科学院 沈阳计算技术研究所有限公司, 沈阳 110168,中国科学院大学, 北京 100049
基金项目:沈阳市2014年科技计划项目(F14-056-7-00);“高档数控机床与基础制造装备”科技重大专项(2013ZX04007031)
摘    要:随着中国制造2025计划的推广,工业电源正处于高速发展期,大型铝电解电容的需求越来越大.电容的故障会令工业流水线中断,产生极大的损失,因此工业电容的寿命预测(RUL)具有重要意义.本文通过对铝电解电容充电状态EIS频谱分析,建立电容的状态的量化模型.根据电容状态量化数据,建立ARMA电容退化预测模型.最后通过美国航天航空局的等效串联电阻EIS频谱数据集进行验证.结果表明ARMA电容退化模型对铝电解电容的状态预测有很大的准确性.

关 键 词:工业铝电解电容  退化预测模型  剩余使用寿命  ARMA  PHM
收稿时间:2016/10/26 0:00:00
修稿时间:2017/2/17 0:00:00

Research on Degradation Study of Industrial Capacity Based on ARMA Model
ZHANG Zheng,TAO Yao-Dong and YU Cheng-Yuan.Research on Degradation Study of Industrial Capacity Based on ARMA Model[J].Computer Systems& Applications,2017,26(7):30-35.
Authors:ZHANG Zheng  TAO Yao-Dong and YU Cheng-Yuan
Affiliation:University of Chinese Academy of Sciences, Beijing 100049, China,University of Chinese Academy of Sciences, Beijing 100049, China;Shenyang Institute of Computing Technology Co. Ltd., CAS, Shenyang 110168, China and University of Chinese Academy of Sciences, Beijing 100049, China
Abstract:With the promotion of "Made in China 2025", the supply in industrial power is in a growing demand for large aluminum electrolytic capacitors. Capacitor failure can interrupt the industrial assembly line, causing a great loss. The prediction of RUL (Remaining Useful Life) of industrial capacitors is hence very important. In this paper, based on the analysis of the EIS of the aluminum electrolytic capacitor, a quantitative model of the state of the capacitor is established. Based on the quantitative data, the degradation prediction model of ARMA capacitor degradation is established. Finally, the equivalent series resistance EIS spectrum data set was verified by NASA. The results show that the short term ARMA capacitor degradation model is accurate in the prediction of the state of aluminum electrolytic capacitors.
Keywords:aluminum electrolytic capacitor  degradation prediction model  remaining useful life  ARMA  PHM
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