基于DE-RLSSVM算法的航空发动机传感器故障诊断

侯宽新; 丁发军; 张道新; 黄选红; 魏武国

doi:10.13224/j.cnki.jasp.2014.12.020

基于DE-RLSSVM算法的航空发动机传感器故障诊断

doi: 10.13224/j.cnki.jasp.2014.12.020

1. 中国民用航空飞行学院航空工程学院, 四川广汉 618307;
2. 中国民用航空飞行学院飞机修理厂, 四川广汉 618307;
3. 中国民用航空飞行学院广汉分院, 四川广汉 618307

详细信息

作者简介:
侯宽新(1979-), 男, 安徽利辛人, 工程师, 硕士, 主要从事民用航空发动机适航与维修技术研究.

中图分类号: V231.3
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- 文章访问数: 1484
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- 被引次数: 0
出版历程
- 收稿日期: 2013-08-14
- 刊出日期: 2014-12-28

Sensor fault diagnosis of aero-engine based on DE-RLSSVM algorithm

1. Aviation Engineering College, Civil Aviation Flight University of China, Guanghan Sichuan 618307, China;
2. Aircraft Repair and Overhaul Plant, Civil Aviation Flight University of China, Guanghan Sichuan 618307, China;
3. Guanghan Sub-College, Civil Aviation Flight University of China, Guanghan Sichuan 618307, China

摘要

摘要: 为了使约简最小二乘支持向量机(RLSSVM)具有更好的稀疏性和泛化能力,利用微分进化(DE)算法选择RLSSVM的支持向量,提出了DE-RLSSVM算法.在benchmark回归数据集上的仿真试验表明该算法具有很好的稀疏性和泛化能力.然后将该算法用于航空发动机传感器故障的诊断,提出了基于DE-RLSSVM算法的航空发动机传感器故障诊断方法.该方法利用DE-RLSSVM算法对传感器故障进行监测,然后进行定位和隔离.数字仿真结果表明该传感器故障诊断系统能够实现对航空发动机传感器硬故障的检测与隔离.
- 航空发动机 /
- 传感器 /
- 故障诊断 /
- 支持向量机 /
- 微分进化
Abstract: In order to improve the generalization capability and sparsity of reduced least square support vector machine (RLSSVM), DE-RLSSVM was presented, whereby differential evolution (DE) algorithm was used to select the support vector of RLSSVM. Experiments on benchmark regression data sets show that the presented algorithm has good generalization and sparsity capabilities. Then, the presented algorithm was applied to sensor fault diagnosis of aero-engine. DE-RLSSVM algorithm was used to detected sensors faults, then to locate and isolate the faults. The simulations show that the sensor fault diagnosis system using the presented algorithm can detect and isolate hardware faults of sensor of aero-engine.
- aero-engine /
- sensor /
- fault diagnosis /
- support vector machine /
- differential evolution

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参考文献(18)

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