基于大规模训练集SVM的发动机故障诊断

徐启华; 耿帅; 师军

基于大规模训练集SVM的发动机故障诊断

1.
淮海工学院电子工程学院, 江苏连云港 222005
2.
西北工业大学第365研究所, 西安 710072

基金项目: 江苏省“六大人才高峰”计划(07-E-029); 江苏省高校科研成果产业化推进项目(JHZD08-40); 江苏省“青蓝工程”学术带头人基金(苏教师(2007)2号)

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出版历程
- 收稿日期: 2011-08-08
- 修回日期: 2011-11-18
- 刊出日期: 2011-12-28

Fault diagnosis method for aero-engine based on SVM with large-scale training set

1.
College of Electronic Engineering, Huaihai Institute of Technology, Lianyungang Jiangsu 222005, China
2.
No.365 Institute, Northwestern Polytechnical University, Xi'an 710072, China

摘要

摘要: 提出了一种新的学习策略,用于解决发动机故障诊断中大规模支持向量机(SVM)的训练问题.通过保留初始SVM分类器支持向量超平面附近的样本以及错分样本,使最终得到的约减集规模明显缩小,从而可在保持较高分类精度的前提下使训练时间明显缩短;同时,由于支持向量的数量减小,分类时间也相应缩短.探讨了序贯最小优化(SMO)算法的参数选择和实现过程中的关键问题,为这种极具潜力的算法在发动机故障诊断中的实际应用奠定了坚实的基础.仿真实例表明,这种基于大规模训练集SVM的发动机故障诊断方法有效、可靠,容易实现,可以作为工程应用的基础.
- 航空发动机 /
- 支持向量机(SVM) /
- 故障诊断 /
- 大规模训练集 /
- 样本约减
Abstract: A learning strategy was presented to solve the large-scale support vector machines (SVM) training problem of aero-engine fault diagnosis.According to the strategy,only those samples were retained for final training,which were near the support vector hyperplane of the original small-scale SVM classifier or classified mistakenly by the original classifier.The final pruning set was reduced evidently and the training time was shortened obviously on the condition of high classification accuracy.Meanwhile,the classification time was shortened correspondingly because of the decrease of the support vector number.Something about the parameter selection of the sequential minimal optimization (SMO) method and how to use this potential algorithm in aero-engine fault diagnosis were also discussed.Simulation examples show that this fault diagnosis method proposed for aero-engine,based on SVM with large-scale training set,is effective,reliable and easy to be implemented for engineering application.
- aero-engine /
- support vector machines(SVM) /
- fault diagnosis /
- large-scale training set /
- sample pruning

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参考文献(16)

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