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基于修正IMM的风机变桨系统故障诊断方法

王进花 朱恩昌 曹洁 余萍

王进花, 朱恩昌, 曹洁, 等 . 基于修正IMM的风机变桨系统故障诊断方法[J]. 北京航空航天大学学报, 2020, 46(8): 1460-1468. doi: 10.13700/j.bh.1001-5965.2019.0526
引用本文: 王进花, 朱恩昌, 曹洁, 等 . 基于修正IMM的风机变桨系统故障诊断方法[J]. 北京航空航天大学学报, 2020, 46(8): 1460-1468. doi: 10.13700/j.bh.1001-5965.2019.0526
WANG Jinhua, ZHU Enchang, CAO Jie, et al. Fault diagnosis method for wind turbine pitch system based on modified IMM[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(8): 1460-1468. doi: 10.13700/j.bh.1001-5965.2019.0526(in Chinese)
Citation: WANG Jinhua, ZHU Enchang, CAO Jie, et al. Fault diagnosis method for wind turbine pitch system based on modified IMM[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(8): 1460-1468. doi: 10.13700/j.bh.1001-5965.2019.0526(in Chinese)

基于修正IMM的风机变桨系统故障诊断方法

doi: 10.13700/j.bh.1001-5965.2019.0526
基金项目: 

国家自然科学基金 61763028

甘肃省自然科学基金 1506RJZA105

详细信息
    作者简介:

    王进花  女, 博士, 副教授。主要研究方向:故障诊断、非线性滤波方法及应用

    朱恩昌   男, 硕士研究生。主要研究方向:故障诊断、智能信息处理

    通讯作者:

    王进花. E-mail: wjh0615@lut.edu.cn

  • 中图分类号: TP277

Fault diagnosis method for wind turbine pitch system based on modified IMM

Funds: 

National Natural Science Foundation of China 61763028

Natural Science Foundation of Gansu Province, China 1506RJZA105

More Information
  • 摘要:

    针对交互式多模型(IMM)故障诊断方法固定模型转移概率导致的诊断准确性、速度下降和估计精度损失问题,提出了一种基于模型转移概率和模型概率修正的故障诊断方法,并与粒子滤波(PF)结合实现了风机变桨系统传感器的多故障诊断。在非模式切换阶段,采用后验模型概率梯度信息设计模型转移概率的修正函数,以抑制噪声对IMM估计精度的影响;在模式切换阶段,采用模型概率反转的策略快速切换模型,弥补模型软切换导致的诊断延迟和错误诊断。通过仿真实验证明所提方法的准确性、模型切换速度以及状态估计精度都得到了较好的提升。

     

  • 图 1  基准模型子系统原理图

    Figure 1.  Schematic diagram of benchmark model subsystem

    图 2  MIMM-PF故障诊断方法流程图

    Figure 2.  Flowchart of MIMM-PF fault diagnosis method

    图 3  仿真实验框架

    Figure 3.  Framework of simulation experiment

    图 4  故障对输出、输入和状态的影响

    Figure 4.  Fault impact on output, input and state

    图 5  故障诊断结果和状态估计

    Figure 5.  Fault diagnosis results and state estimation

    图 6  模型概率变化曲线

    Figure 6.  Model probability variation curves

    表  1  传感器故障模型

    Table  1.   Sensor failure models

    模型 传感器故障类型 故障建模
    M1 正常 fs, k=0
    M2 恒增益 fs, k=βbias
    M3 恒偏差 fs, k=-h(·)+kgainh(·)
    M4 卡死 fs, k=-h(·)+βfixedvk
    注:βbiaskgainβfixed分别为桨距角测量偏差值、增益系数和固定值。
    下载: 导出CSV

    表  2  MIMM-PF与标准IMM-PF性能对比

    Table  2.   Performance comparison between MIMM-PF and standard IMM-PF

    指标 IMM-PF MIMM-PF
    CDID 564.54(94.09%) 582.76(97.13%)
    Delay 6.36ΔT 3.6833ΔT
    RMSE 1.2438 0.5434
    注:()内为CDID的百分比形式。
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-09-26
  • 刊出日期:  2020-08-20

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