基于自适应RVM的电子系统缓变故障预测方法

范庚; 马登武; 张继军; 吴明辉

基于自适应RVM的电子系统缓变故障预测方法

1.
海军航空工程学院兵器科学与技术系, 烟台 264001
2. 海军航空工程学院科研部, 烟台 264001

基金项目: 武器装备预研基金资助项目(9140A27020212JB14311)

详细信息

作者简介:
范庚(1985-),男,山东临沂人,博士生,meteras@163.com.

中图分类号: TP206
计量
- 文章访问数: 1394
- HTML全文浏览量: 201
- PDF下载量: 471
- 被引次数: 0
出版历程
- 收稿日期: 2012-07-02
- 网络出版日期: 2013-10-30

Gradual fault prediction method for electronic system based on adaptive RVM

1.
Department of Ordnance Science and Technology, Naval Aeronautical and Astronautical University, Yantai 264001, China
2. Department of Scientific Research, Naval Aeronautical and Astronautical University, Yantai 264001, China

摘要

摘要: 针对电子系统缓变故障的预测问题,提出一种自适应相关向量机(RVM, Relevance Vector Machine)方法.首先,对反映电子系统性能的参数序列进行相空间重构,建立RVM的输入输出对应关系;然后,将嵌入维数和核函数参数作为人工鱼位置,取留一交叉验证(LOOCV, Leave-One-Out Cross-Validation)误差的相反数作为目标函数,利用人工鱼群算法(AFSA, Artificial Fish Swarm Algorithm)实现方法参数的自适应优化选择;最后,通过雷达发射机高压电源与多注速调管的故障预测实验验证了方法的性能.实验结果表明:该方法在预测精度和预测可靠性方面优于现有方法.
- 故障预测 /
- 电子系统 /
- 相关向量机 /
- 自适应优化 /
- 人工鱼群算法 /
- 留一交叉验证
Abstract: Aiming at gradual fault prediction of electronic system, a method based on adaptive relevance vector machine (RVM) was proposed. Firstly, the phase space of electronic system performance parameter time series was reconstructed, and then the corresponding relationship between the input and the output of RVM was educed. Secondly, the artificial fish swarm algorithm (AFSA) was adopted to realize the adaptive optimization of method parameters, which took the embedding dimension and the RVM kernel parameter as the artificial fish position and took the opposite number of leave-one-out cross-validation (LOOCV) error as the objective function. Lastly, the performance of the proposed method was validated by radar transmitter fault prediction experiment. The experimental results show that the proposed method has better accuracy and reliability than the existed methods.
- fault prediction /
- electronic system /
- relevance vector machine /
- adaptive optimization /
- artificial fish swarm algorithm /
- leave-one-out cross-validation

HTML全文

参考文献(1)

[1] 景博,黄以锋,张建业.航空电子系统故障预测与健康管理技术现状与发展[J].空军工程大学学报:自然科学版,2010, 11(6) :1-6 Jing Bo,Huang Yifeng,Zhang Jianye.Status and perspectives of prognostics and health management technology of avionics system [J].Journal of Air Force Engineering University:Natural Science Edition,2010,11(6):1-6(in Chinese) [2] 许丽佳.电子系统的故障预测与健康管理技术研究[D].成都:电子科技大学自动化学院,2005 Xu Lijia.Study on fault prognostic and health management for electronic system [D].Chengdu:Automation Engineering Institute,University of Electronic Science and Technology of China,2005(in Chinese) [3] 张弦,王宏力,张金生,等.状态时间序列预测的贝叶斯最小二乘支持向量机方法[J].西安交通大学学报,2010, 44(10): 42-46 Zhang Xian,Wang Hongli,Zhang Jinsheng,et al.A least squares support vector machine for condition time series prediction based on Bayesian evidence framework [J].Journal of Xi-an Jiaotong University,2010,44(10):42-46(in Chinese) [4] 张弦,王宏力.局域极端学习机及其在状态在线监测中的应用[J].上海交通大学学报,2011,45(2):236-240 Zhang Xian,Wang Hongli.Local extreme learning machine and its application to condition online monitoring [J].Journal of Shanghai Jiaotong University,2011,45(2):236-240(in Chinese) [5] Tipping M E.The relevance vector machine[C]//Advances in Neural Information Processing Systems 12.Cambridge:MIT Press,2000:652-658 [6] Tipping M E.Sparse Bayesian learning and the relevance vector machine [J].Journal of Machine Learning Research,2001, 1(3): 211-244 [7] Tipping M E,Faul A C.Fast marginal likelihood maximisation for sparse Bayesian models[C]//Proc of the Ninth International Workshop on Artificial Intelligence and Statistics.Key West,Florida,USA: [s.n.],2003:1-13 [8] 仕玉治,彭勇,周惠成.基于相关向量机的中长期径流预报模型研究[J].大连理工大学学报,2012,52(1):79-84 Shi Yuzhi,Peng Yong,Zhou Huicheng.Research on mid-and long-term runoff forecast model with relevance vector machine [J].Journal of Dalian University of Technology,2012,52(1):79-84(in Chinese) [9] 黄帅栋,卫志农,高宗和,等.基于非负矩阵分解的相关向量机短期负荷预测模型[J].电力系统自动化,2012,36(11):62-66 Huang Shuaidong,Wei Zhinong,Gao Zonghe,et al.A short-term load forecasting model based on relevance vector machine with nonnegative matrix factorization [J].Automation of Electric Power Systems,2012,36(11):62-66(in Chinese) [10] Goebel K,Saha B,Saxena A.A comparison of three data-driven techniques for prognostics[C]// Proc of the 62 nd Meeting of the Society For Machinery Failure Prevention Technology (MFPT).Virginia Beach,Virginia,USA:[s.n.],2008:119-131 [11] 盛骤,谢式千,潘承毅.概率论与数理统计[M].北京:高等教育出版社,1989:53-55 Sheng Zhou,Xie Shiqian,Pan Chengyi.Probability and mathematical statistics [M].Beijing:Higher Education Press,1989:53-55(in Chinese) [12] Refaeilzadeh P,Tang L,Liu H.Encyclopedia of database systems:cross-validation [M].Ozsu M T,Liu L.US:Springer,2009:532-538 [13] 李晓磊,邵之江,钱积新.一种基于动物自治体的寻优模式:鱼群算法[J].系统工程理论与实践,2002(11):32-38 Li Xiaolei,Shao Zhijiang,Qian Jixin.An optimizing method based on autonomous animats:fish-swarm algorithm [J].Systems Engineering—Theory & Practice,2002(11):32-38(in Chinese) [14] 俞洋,殷志锋,田亚菲.基于自适应人工鱼群算法的多用户检测器[J].电子与信息学报,2007,29(1):121-124 Yu Yang,Yin Zhifeng,Tian Yafei.Multiuser detector based on adaptive artificial fish school algorithm [J].Journal of Electronics & Information Technology,2007,29(1):121-124(in Chinese) [15] 何英,周东华,俞容.一种基于性能退化数据的电子设备缓变故障预报方法[J].仪器仪表学报,2008,29(7):1526-1529 He Ying,Zhou Donghua,Yu Rong.Gradual failure prediction of electronic equipment based on performance degradation data [J].Chinese Journal of Scientific Instrument,2008,29(7):1526-1529(in Chinese) [16] 姜媛媛,王友仁,崔江,等.基于LS-SVM的电力电子电路故障预测方法[J].电机与控制学报,2011,15(8):64-68 Jiang Yuanyuan,Wang Youren,Cui Jiang,et al.Research on fault prediction method of power electronic circuits based on least squares support vector machine [J].Electric Machines and Control,2011,15(8):64-68(in Chinese) [17] 薛辉辉,肖明清,段军峰.基于杂合支持向量回归机的电子装备故障预测[J].计算机工程,2012,38(8):283-286 Xue Huihui,Xiao Mingqing,Duan Junfeng.Fault prediction for electronic equipment based on hybrid support vector regression[J].Computer Engineering,2012,38(8):283-286(in Chinese)

施引文献

资源附件(0)

访问统计