计算机科学

计算机科学 ›› 2020, Vol. 47 ›› Issue (11A): 132-138.doi: 10.11896/jsjkx.200600101

• 人工智能 • 上一篇    下一篇

基于双变异粒子群优化算法优化的支持向量机及其在民航发动机损伤类型识别中的应用

郑波1, 马昕2   

  1. 1 中国民航飞行学院教务处 四川 广汉 618307
    2 中国民航飞行学院空中交通管理学院 四川 广汉 618307
  • 出版日期:2020-11-15 发布日期:2020-11-17
  • 通讯作者: 马昕(ttccll123321@126.com)
  • 作者简介:b_zheng1@126.com
  • 基金资助:
    四川省科技计划项目(2019YJ0720);中国民用航空局发展基金教育人才类项目(14002600100018J034);中国民航飞行学院面上项目(2019-53);中国民航飞行学院青年基金项目(Q2018-139)

Application on Damage Types Recognition in Civil Aeroengine Based on SVM Optimized by DMPSO

ZHENG Bo1, MA Xin2   

  1. 1Academic Affairs Office,Civil Aviation Flight University of China,Guanghan,Sichuan 618307,China
    2 College of Air Traffic Control,Civil Aviation Flight University of China,Guanghan,Sichuan 618307,China
  • Online:2020-11-15 Published:2020-11-17
  • About author:ZHENG Bo,born in 1984,Ph.D,asso-ciate professor.His main research interests include fault diagnosis,pattern recognition and optimization design.
    MA Xin,born in 1984,B.S., lecturer.His main research interests include optimization design of civil aviation traffic safetyand flight reliability diagnosis.
  • Supported by:
    This work was supported by the Project of Sichuang Province Science and Technology Program (2019YJ0720),China Civil Aviation Administration Development Foundation Educational Talents Program (14002600100018J034),General Foundation of Civil Aviation Flight University of China(2019-053) and Youth Foundation of Civil Aviation Flight University of China(Q2018-139).

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摘要： 为提高民航发动机损伤类型识别的自动化水平和可靠度,增强民航发动机的维修保障能力,本文利用颜色矩和灰度共生矩阵(Gray Level Co-occurrence Matrix,GLCM)来构造基于发动机无损检测图像的特征数据库,同时将支持向量机(Support Vector Machine,SVM)作为智能识别算法。为保障SVM可靠稳定的识别性能,提出利用双变异的粒子群优化 (Dual Mutation Particles Swarm Optimization,DMPSO)算法对核参数和惩罚因子进行优化,双变异策略提升了PSO的全局寻优能力,一些复杂的测试函数验证了DMPSO的全局寻优能力。最后根据某型发动机的4种损伤类型图像,按照不同的特征提取方法构造特征数据库,分别利用本文所提的DMPSO优化的SVM、BP(back propagation)网络、ELM(Extreme Learning Machines)网络以及k-NN(k-nearest neighborhood)算法进行损伤类型识别,识别结果证明了文中所提的特征提取方法更适合发动机损伤识别,有利于提高损伤识别精度。同时比较了4种识别算法的性能,基于DMPSO优化的SVM具有更优、更稳定的识别输出。对比实验证明了所提方法有利于提升民航发动机损伤类型的识别效率。

关键词: 灰度共生矩阵, 粒子群优化算法, 损伤类型识别, 颜色矩, 支持向量机

Abstract: In order to recognize the damage types of aeroengine automatically and reliably,enhance the capability of aeroengine maintenance support,the feature extraction method based on color moments and gray level co-occurrence matrix (GLCM) is proposed to construct the feature database of the aeroengine's non-destructive detection images,and the support vector machine (SVM) is utilized as intelligent classifier for damages recognition.A dual mutation particles swarm optimization (DMPSO) algorithm is designed to optimize the kernel parameter and penalty factor for guaranteeing the recognition performance of SVM,dual mutation strategy improves the global optimization capability,and some complex test functions have been used to prove DMPSO'sperformance.Finally,the feature databases are constructed by different feature methods according to four damage types of certain aeroengine,and then the proposed SVM optimized by DMPSO is used for damage types recognition compared with back propagation (BP) network,extreme learning machine (ELM) network,and k-nearest neighborhood (k-NN).The recognition results have proven the proposed feature extraction method is more suitable for aeroengine damage recognition and is helpful to improve the accuracy of damage recognition.Meanwhile,the recognition performances of the four algorithms are compared,and the comparison results have demonstrated the optimized SVM always has better and stable recognition output.The comparison experiment has proven that the methods proposed in this paper are helpful to improve the recognition efficiency of aeroengine damage types.

Key words: Color moments, Damage types recognition, GLCM, PSO, SVM

中图分类号: 

  • V263.6
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