论文:2017,Vol:35,Issue(4):724-728
引用本文:
李飞, 陈颖, 郭阳明, 杜承烈, 吴昊, 冉从宝. 基于多核LS-SVR的航电设备剩余寿命预测[J]. 西北工业大学学报
Li Fei, Chen Ying, Guo Yangming, Du Chenglie, Wu Hao, Ran Congbao. Avionics Remain Life Prediction Using Multiple Kernel LS-SVR[J]. Northwestern polytechnical university

基于多核LS-SVR的航电设备剩余寿命预测
李飞1, 陈颖2, 郭阳明1, 杜承烈1, 吴昊1, 冉从宝1
1. 西北工业大学 计算机学院, 陕西 西安 710072;
2. 中国船舶工业系统工程研究院, 北京 100094
摘要:
航空电子设备是飞机系统的重要组成部分,其故障率占全系统故障总数的比例越来越高,对系统性能的影响越来越突出。论文提出了一种基于多核LS-SVR的预测模型,并用于某航电装置的剩余寿命预测。仿真结果表明,该多核LS-SVR模型与传统LS-SVR相比,具有更高的精度,是一个切实有效的电子设备寿命预测方法。
关键词:    航空电子设备    剩余寿命预测    LS-SVR    多核学习   
Avionics Remain Life Prediction Using Multiple Kernel LS-SVR
Li Fei1, Chen Ying2, Guo Yangming1, Du Chenglie1, Wu Hao1, Ran Congbao1
1. School of Software and Microelectronics, Northwestern Polytechnical University, Xi'an 710072, China;
2. Systems Engineering Research Institute of CSSC, Beijing 100094, China
Abstract:
Avionics equipments are important parts of the aircraft system, and their failure probability is higher and higher, which will affect the performance of the whole system. A prediction model based on MKLS-SVR is proposed in this paper and used for remain life prediction with an avionic device. The simulation results show that the MKLS-SVR has a higher accuracy, comparing with the traditional model LS-SVR, and it is a practical and effective electronic equipment life prediction method.
Key words:    Matlab    root mean square error(RMSE)    support vector machines(SVM)    time series   
收稿日期: 2017-01-10     修回日期:
DOI:
基金项目: 国家自然科学基金(61371024、61601371)、航空科学基金(2016ZD53035)、中航产学研项目(cxy2013XGD14)、航天支撑技术基金(2015-HT-XGD)与电子元器件可靠性物理及应用技术重点实验室开放基金资助
通讯作者:     Email:
作者简介: 李飞(1982—),西北工业大学助理研究员,主要从事健康管理技术研究。
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