基于健康度分析与和声蚁群算法-支持向量机的故障预测模型

doi:10.11772/j.issn.1001-9081.2015.11.3252

计算机应用 ›› 2015, Vol. 35 ›› Issue (11): 3252-3255.DOI: 10.11772/j.issn.1001-9081.2015.11.3252

基于健康度分析与和声蚁群算法-支持向量机的故障预测模型

邱文昊¹, 黄考利², 金赛赛³, 连光耀²

1. 军械工程学院四系, 石家庄 050003;
2. 军械技术研究所一室, 石家庄 050003;
3. 航天飞行控制中心, 北京 100094

收稿日期:2015-06-04 修回日期:2015-08-04 发布日期:2015-11-13
通讯作者: 邱文昊(1990-),男,山东滕州人,硕士研究生,主要研究方向:故障预测、健康管理.
作者简介:黄考利(1958-),男,山东潍坊人,研究员,博士,主要研究方向:故障预测、测试性验证; 金赛赛(1990-),男,山东济宁人,助理工程师,硕士,主要研究方向:故障预测与处理; 连光耀(1980-),男,河南宝丰人,高级工程师,博士,主要研究方向:装备测试与诊断.
基金资助:
国防预先研究科研项目(51327030104).

Fault prediction model based on health analysis and harmony search-ant colony algorithm-support vector machine

QIU Wenhao¹, HUANG Kaoli², JIN Saisai³, LIAN Guangyao²

1. Department No.4, Ordnance Engineering College, Shijiazhuang Hebei 050003, China;
2. Research Room No.1, Ordnance Technological Research Institute, Shijiazhuang Hebei 050003, China;
3. Space Flight Control Center, Beijing 100094, China

Received:2015-06-04 Revised:2015-08-04 Published:2015-11-13

摘要/Abstract

摘要： 针对现有的故障预测技术无法从整体上反映系统性能下降趋势等问题,提出一种基于健康度分析的故障预测方法.首先,在支持向量机回归算法基础上构造多输出支持向量机,以实现健康度的多步预测,并提出一种和声蚁群算法优化支持向量机参数,解决了蚁群算法易陷入局部最优的问题; 然后, 根据最优参数建立拟合监测数据和未来健康度下降过程非线性映射关系的和声蚁群算法-支持向量机(HSACA-SVM)故障预测模型; 最后,通过某装备电源系统监测数据验证了该模型的有效性.实例验证表明该模型能够较好地实现对健康度下降趋势的预测,预测准确率达到97%,进而实现故障预测.

关键词: 健康度分析, 和声算法, 蚁群算法, 多输出支持向量机, 故障预测

Abstract: A new method of fault prediction based on health analysis was proposed for the problem of the existing fault prediction technology could not response the declining trend of system property as whole. Firstly, in order to achieve multi-step prediction, multiple output Support Vector Machine (SVM) was formatted on the basis of support vector machine regression algorithm, while using the Harmony Search-Ant Colony Algorithm (HSACA) to optimize parameters of SVM to solve the local optimal problem. Then nonlinear mapping Harmony Search-Ant Colony Algorithm-Support Vector Machine (HSACA-SVM) model matching monitoring data and health degree was built with the optimal parameters. Finally, the proposed model was used to evaluate a power supply system. The results indicate that the HSACA-SVM model can predict the downward trend of health degree with 97% accuracy, and then realize fault prediction.

Key words: health analysis, Harmony Search Algorithm (HSA), Ant Colony Algorithm (ACA), multiple output Support Vector Machine (SVM), fault prediction

中图分类号:

TP206

邱文昊, 黄考利, 金赛赛, 连光耀. 基于健康度分析与和声蚁群算法-支持向量机的故障预测模型[J]. 计算机应用, 2015, 35(11): 3252-3255.

QIU Wenhao, HUANG Kaoli, JIN Saisai, LIAN Guangyao. Fault prediction model based on health analysis and harmony search-ant colony algorithm-support vector machine[J]. Journal of Computer Applications, 2015, 35(11): 3252-3255.

参考文献

[1] WU Y, WANG Y, JIANG Y, et al. Fault prediction method of DC/DC converter based on characteristic parameter degradation [J]. Chinese Journal of Scientific Instrument, 2013, 34(6): 1380-1387. (吴祎,王友仁,姜媛媛,等. 基于特征参数退化的DC/DC变换器故障预测[J].仪器仪表学报, 2013,34(6):1380-1387.)
[2] TU W, ZHANG G. Application of SVM multi-class classification in transformer fault diagnosis [J]. Journal of Computer Applications, 2010, 30(1): 96-98. (徒伟,张广明. 基于支持向量机的多类分类在变压器故障诊断中的应用[J]. 计算机应用, 2010, 30(1): 96-98.)
[3] DANG X, JIANG T. Degradation prediction based on correlation analysis and assembled neural network [J]. Journal of Beijing University of Aeronautics and Astronautics, 2013, 39(1): 42-46. (党香俊,姜同敏. 基于相关分析和组合神经网络的退化预测[J]. 北京航空航天大学学报, 2013, 39(1): 42-46.)
[4] ZHANG L, WANG Z, ZHAN S. Short-time fault prediction of mechanical rotating parts on the basis of fuzzy-grey optimizing method [J]. Mechanical Systems and Signal Processing, 2007, 21:856-865.
[5] NIE J, ZHANG W. Study of fault forecast of low-voltage switchgear assembly based on fuzzy theory [J]. Application Research of Computers, 2009, 26(1): 215-217. (聂俊岚,张伟.基于模糊理论的低压开关设备故障预测研究[J].计算机应用研究, 2009, 26(1): 215-217.)
[6] DING S, QI B, TAN H. An overview on theory and algorithm of support vector machines [J]. Journal of University of Electronic Science and Technology of China, 2011, 40(1): 2-10. (丁世飞,齐丙娟,谭红艳. 支持向量机理论与算法研究综述[J]. 电子科技大学学报,2011,40(1):2-10.)
[7] DENG N, TIAN Y. Theory of support vector machine [M]. Beijing: Science Press, 2009: 43-56. (邓乃扬,田英杰.支持向量机理论、算法与拓展[M].北京: 科学出版社,2009: 43-56.)
[8] YANG H, ZHOU Y, LIU H. Chaos optimization SVR algorithm with application in prediction of regional logistics demand[C]// Proceedings of the 1st International Conference Advances in Swarm Intelligence, LNCS 6146. Berlin: Springer-Verlag, 2010: 58-64.
[9] SHI X, LIU H, YU S. Short-time load prediction based on support vector machine optimized by catfish particle swarm optimization algorithm [J]. Computer Engineering and Applications, 2013, 49(11): 220-223.(石晓艳,刘淮霞,于水娟. 鲶鱼粒子群算法优化支持向量机的短期负荷预测[J]. 计算机工程与应用,2013, 49(11):220-223.)
[10] GENG J, SUN L, CHEN S. Parameters optimization of combined kernel function for support vector machine [J]. Journal of Computer Applications, 2013,33(5): 1321-1323.(耿俊豹,孙林凯,陈是学. 支持向量机的混合核函数参数优选方法[J]. 计算机应用, 2013,33(5): 1321-1323.)
[11] ZHANG Y, ZHANG X, TANG L. Energy consumption prediction in ironmaking process using hybrid algorithm of SVM and PSO[J]. Proceedings of the 9th international conference on Advances in Neural Networks, LNCS 7368. Berlin: Springer-Verlag, 2012: 601-610.
[12] SUN X, LU H, WU J. Passenger traffic volume forecasting based on support vector machine model optimized by ant colony algorithm [J]. Journal of Hefei University of Technology: Natural Science, 2012, 35(1): 125-129.(孙煦,陆化普,吴娟. 基于蚁群优化支持向量机模型的公路客运量预测[J]. 合肥工业大学学报:自然科学版,2012,35(1):125-129.)
[13] GUAN X, GUO Q, ZHANG Z, et al. Radar emitter signal recognition based on kernel function SVM [J]. Journal of Projectiles, Rockets, Missiles and Guidance, 2011, 31(4):188-194.(关欣,郭强,张政超,等. 基于核函数支持向量机的雷达辐射源识别[J]. 弹箭与制导学报,2011,31(4):188-191.)
[14] COLORNI A, DORIGO M, MANIEZZO V, et al. Distributed optimization by ant colonies[C]// Proceedings of the 1st European Conference on Artificial Life. Cambridge: MIT Press, 1991: 134-142.
[15] MAHDAVI M, FESANGHARY M, XDAMANGIR M. An improved harmony search algorithm for solving optimization problems [J]. Applied Mathematics and Computation, 2007, 188(2):1567-1579.
[16] LI X, LIU Y, LI G, et al. Assessment of satellite health state based on fuzzy variable weight theory[J]. Systems Engineering and Electronics, 2014,36(3):476-480. (李鑫,刘莹莹,李赣华,等. 基于模糊变权原理的卫星健康评估方法[J]. 系统工程与电子技术,2014,36(3):476-480.)

基于健康度分析与和声蚁群算法-支持向量机的故障预测模型

Fault prediction model based on health analysis and harmony search-ant colony algorithm-support vector machine

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