基于NN-ELM的航空发动机燃油系统执行机构故障诊断

姜洁; 李秋红; 张高钱; 李业波

doi:10.13224/j.cnki.jasp.2016.02.029

基于NN-ELM的航空发动机燃油系统执行机构故障诊断

doi: 10.13224/j.cnki.jasp.2016.02.029

南京航空航天大学能源与动力学院江苏省航空动力系统重点实验室, 南京 210016

基金项目:

航空科学基金(20110652003)

江苏高校优势学科建设工程资助项目

中央高校基本科研业务专项基金(NN2012033)

详细信息

作者简介:
姜洁(1990-),女,江苏南通人,硕士生,主要研究方向为航空发动机故障诊断.

中图分类号: V233.7
计量
- 文章访问数: 1077
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- 被引次数: 0
出版历程
- 收稿日期: 2014-06-18
- 刊出日期: 2016-02-28

Fault diagnosis for actuators of aero-engine fuel system based on NN-ELM

Jiangsu Province Key Laboratory of Aerospace Power System, College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China

摘要

摘要: 提出了一种航空发动机执行机构及其传感器单一故障诊断及定位方法.首先通过执行机构模型判断是否发生故障,然后运用发动机逆模型对故障进行定位.基于离线训练BP(back propagation)神经网络建立执行机构模型,根据某半物理仿真试验台的测试数据训练网络参数.提出离线训练和在线训练相结合的极端学习机(ELM)算法建立发动机逆模型,使网络在初始时刻就具有诊断能力,工作过程中具有适应能力,且在线训练过程采用阈值判别法筛选训练样本,减小了在线训练时间,提高了逆模型的实时性.以某型发动机燃油系统执行机构为例的设计和仿真结果表明:该诊断系统能够准确地对发动机在稳态和动态工况以及蜕化状态下的执行机构及其传感器单一故障进行准确诊断和定位,具有很好的实时性.
- 航空发动机 /
- 执行机构 /
- 故障诊断 /
- 神经网络 /
- 极端学习机
Abstract: A single fault diagnosis and location method of aero-engine actuator and its sensor was proposed. Fault of actuator was diagnosed according to the actuator model and located according to the inverse engine model. The actuator model was built based on offline training BP(back propagation) neural network (NN). The network parameters were trained according to the test data of a semi-physical simulation test bed. The inverse engine model based on offline and online extreme learning machine (ELM) had original diagnosis ability and adaptability during operation. The training samples were selected using the threshold discrimination, which can reduce the online training time and improve the real-time property of inverse model during the online training process. Design and simulation results of a certain engine fuel system actuator show that,the proposed system can diagnose and locate the single faults of engine actuator or its sensor accurately in steady state, dynamic state and degradation state. The real-time property can be satisfied.
- aero-engine /
- actuator /
- fault diagnosis /
- neural network (NN) /
- extreme learning machine

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