东北大学学报（自然科学版）

东北大学学报(自然科学版) ›› 2012, Vol. 33 ›› Issue (4): 571-575.DOI: -

• 论著 • 上一篇    下一篇

基于DNPSO的支持向量机的发动机故障诊断

聂立新;张天侠;张丽萍;郭立新;   

  1. 东北大学机械工程与自动化学院;
  • 收稿日期:2013-06-19 修回日期:2013-06-19 发布日期:2013-04-04
  • 通讯作者: -
  • 作者简介:-
  • 基金资助:
    国家自然科学基金资助项目(50875041);;

DNPSO-based support vector machine for engine fault diagnosis

Nie, Li-Xin (1); Zhang, Tian-Xia (1); Zhang, Li-Ping (1); Guo, Li-Xin (1)   

  1. (1) School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China
  • Received:2013-06-19 Revised:2013-06-19 Published:2013-04-04
  • Contact: Nie, L.-X.
  • About author:-
  • Supported by:
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摘要： 设定了基于粒子序号和粒子邻居数量的动态邻域粒子群模式,并通过田口试验分析了6种测试函数的优化性能,选定了粒子群算法的惯性权重、粒子邻居数量及加速系数等参数的较优渐变模式,建立了具有较为广泛适应性且运算量相对较低的动态邻域粒子群模型.利用该模型优化了支持向量机的惩罚参数和核函数评估参数,在发动机的故障特征识别过程中,通过与BP神经网络及标准粒子群算法优化参数的支持向量机等分类器的比较,动态邻域粒子群算法优化的支持向量机具有较高的特征识别能力和较强的鲁棒性.

关键词: 支持向量机, 粒子群优化算法, 动态邻域, 田口试验, 惩罚参数, 核函数评估参数, 故障诊断

Abstract: A dynamic neighborhood particle sware pattern was set on the basis of serial numbers and amount of neighboring particles. The optimum performances of six test functions were analyzed by the Taguchi DOE. The better change pattrens of the PSO parameters, such as inertia weight, number of the particle neighbors and acceleration coefficients, were selected. An optimization model of dynamic neighborhood particle swarm was built, which may have a wider adaptability and lower computation complexity. The penalty parameters and kernel function evaluation parameters of support vector machine were optimized with the proposed model. Compared with that of the BP ANN and SPSO-based SVM models, DNPSO-based support vector machine shows a better characteristics identification ability and higher robustness in the engine fault diagnosis.

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