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基于DWNN的机电作动器渐变性故障诊断

王剑 王新民 谢蓉 曹宇燕 李婷

王剑, 王新民, 谢蓉, 等 . 基于DWNN的机电作动器渐变性故障诊断[J]. 北京航空航天大学学报, 2019, 45(9): 1831-1837. doi: 10.13700/j.bh.1001-5965.2018.0769
引用本文: 王剑, 王新民, 谢蓉, 等 . 基于DWNN的机电作动器渐变性故障诊断[J]. 北京航空航天大学学报, 2019, 45(9): 1831-1837. doi: 10.13700/j.bh.1001-5965.2018.0769
WANG Jian, WANG Xinmin, XIE Rong, et al. Gradual fault diagnosis for electromechanical actuator based on DWNN[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(9): 1831-1837. doi: 10.13700/j.bh.1001-5965.2018.0769(in Chinese)
Citation: WANG Jian, WANG Xinmin, XIE Rong, et al. Gradual fault diagnosis for electromechanical actuator based on DWNN[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(9): 1831-1837. doi: 10.13700/j.bh.1001-5965.2018.0769(in Chinese)

基于DWNN的机电作动器渐变性故障诊断

doi: 10.13700/j.bh.1001-5965.2018.0769
基金项目: 

国家自然科学基金 61703341

详细信息
    作者简介:

    王剑  男, 博士研究生。主要研究方向:飞行器机电系统故障诊断技术

    王新民  男, 教授, 博士生导师。主要研究方向:飞行器控制技术、飞行器机电系统故障诊断技术

    谢蓉  女, 博士, 副教授。主要研究方向:飞行器控制技术、飞行器健康管理技术

    曹宇燕  女, 博士研究生。主要研究方向:飞行器机电系统故障诊断技术

    李婷  女, 博士研究生。主要研究方向:飞行器机电系统故障诊断技术

    通讯作者:

    谢蓉, E-mail: xierong@nwpu.edu.cn

  • 中图分类号: V242.5;TP277

Gradual fault diagnosis for electromechanical actuator based on DWNN

Funds: 

National Natural Science Foundation of China 61703341

More Information
  • 摘要:

    飞机飞行控制系统机电作动器(EMA)的渐变性故障很难准确预判,若不能及早发现而任其发展就会影响到飞机的飞行安全性。针对EMA的渐变性故障,提出一种基于动态小波神经网络(DWNN)的故障诊断方法。首先,利用EMA在电机电枢绕组匝间短路、传动装置丝杆和滚珠磨损等多种渐变性故障状态下的运行数据来训练DWNN故障诊断模型;然后,利用训练好的DWNN模型对EMA渐变性故障进行诊断。创新之处在于DWNN模型利用小波分解算法去除了传感器测量信号中高频分量的影响,利用反馈神经网络的记忆能力融合了过去输入的信息和过去预测的信息,提高了对EMA渐变性故障诊断的准确性。通过对某型EMA进行故障诊断实验,仿真结果表明所提出的DWNN方法可以实现对EMA部件渐变性故障的准确诊断。

     

  • 图 1  DWNN基本结构

    Figure 1.  Basic structure of DWNN

    图 2  DUKF训练框架

    Figure 2.  Training framework of DUKF

    图 3  绕组匝间短路故障特征参数变化曲线

    Figure 3.  Fault characteristic parameter variation curves of interturn short circuit of winding

    图 4  传动机构磨损故障特征参数变化曲线

    Figure 4.  Fault characteristic parameter variation curves of wear of transmission device

    表  1  故障特征

    Table  1.   Fault features

    故障模式 故障特征参数变化
    Bm Jm Ra La
    传动机构过度磨损 +10%
    控制面损失 -10%
    电枢绕组匝间短路 -10% -10%
    注:“+”表示特征参数增大;“-”表示特征参数减小。
    下载: 导出CSV
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出版历程
  • 收稿日期:  2018-12-29
  • 录用日期:  2019-02-02
  • 刊出日期:  2019-09-20

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