北京航空航天大学学报 ›› 2017, Vol. 43 ›› Issue (7): 1419-1425.doi: 10.13700/j.bh.1001-5965.2016.0553

• 论文 • 上一篇    下一篇

免疫支持向量机用于航空发动机磨损故障诊断

张建, 李艳军, 曹愈远, 张丽娜   

  1. 南京航空航天大学 民航学院, 南京 211106
  • 收稿日期:2016-06-27 修回日期:2016-09-21 出版日期:2017-07-20 发布日期:2016-11-16
  • 通讯作者: 李艳军,E-mail:lyj@nuaa.edu.cn E-mail:lyj@nuaa.edu.cn
  • 作者简介:张建 男,硕士研究生。主要研究方向:故障诊断与健康监测、适航技术与管理;李艳军 男,博士,教授,硕士生导师。主要研究方向:故障诊断与健康监测、适航技术与管理。
  • 基金资助:
    航空科学基金(20153352040);南京航空航天大学校开放基金(kfjj20150701)

Immune SVM used in wear fault diagnosis of aircraft engine

ZHANG Jian, LI Yanjun, CAO Yuyuan, ZHANG Lina   

  1. College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
  • Received:2016-06-27 Revised:2016-09-21 Online:2017-07-20 Published:2016-11-16
  • Supported by:
    Aeronautical Science Foundation of China (20153352040); Open Foundation of Nanjing University of Aeronautics and Astronautics(kfjj20150701)

摘要: 航空发动机在使用寿命周期内会不断磨损最终出现故障,通过对发动机油液监测铁谱分析数据的挖掘可实现磨损故障的诊断。本文研究免疫算法优化的支持向量机(SVM)在航空发动机磨损故障诊断中的运用。首先,总结了支持向量机和免疫算法的运行流程和关键算法。然后,用改进的免疫算法优化支持向量机惩罚因子、松弛变量及核函数参数。某型航空发动机的油液铁谱分析数据和加入噪声数据验证结果表明,该方法可有效实现航空发动机磨损故障诊断且具有较好的鲁棒性。最后,研究了核函数、多分类决策方法、初始种群大小、亲和力计算公式、支持向量机优化方法和归一化方法对磨损故障诊断准确率的影响,得到了最佳诊断方法。

关键词: 航空发动机, 磨损故障诊断, 铁谱分析, 免疫算法, 支持向量机(SVM)

Abstract: Aircraft engine is wearing during its service life and will finally break down. The wear fault can be diagnosed by analyzing the ferrography data of oil monitoring. The use of immune algorithm optimized support vector machine (SVM) in aircraft engine wear fault diagnosis was researched in this paper. First, the process and algorithm of SVM and immune algorithm were summarized. Then, the optimization of SVM's penalty factor, slack variable and kernel function parameters by immune algorithm was researched. The verification results of an engine's oil ferrography analysis data and adding noise data show that the method can effectively diagnose the aircraft engine wear fault and has good robustness. Finally, the impact of kernel function, multi-classification decision method, initial population size, affinity calculation formula, optimization algorithm and normalization method on diagnosis accuracy was analyzed, and the best algorithm was achieved.

Key words: aircraft engine, wear fault diagnosis, ferrography analysis, immune algorithm, support vector machine (SVM)

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