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基于诊断证据静态融合与动态更新的故障诊断方法

徐晓滨 张镇 李世宝 文成林

徐晓滨, 张镇, 李世宝, 文成林. 基于诊断证据静态融合与动态更新的故障诊断方法. 自动化学报, 2016, 42(1): 107-121. doi: 10.16383/j.aas.2016.c150403
引用本文: 徐晓滨, 张镇, 李世宝, 文成林. 基于诊断证据静态融合与动态更新的故障诊断方法. 自动化学报, 2016, 42(1): 107-121. doi: 10.16383/j.aas.2016.c150403
XU Xiao-Bin, ZHANG Zhen, LI Shi-Bao, WEN Cheng-Lin. Fault Diagnosis Based on Fusion and Updating of Diagnosis Evidence. ACTA AUTOMATICA SINICA, 2016, 42(1): 107-121. doi: 10.16383/j.aas.2016.c150403
Citation: XU Xiao-Bin, ZHANG Zhen, LI Shi-Bao, WEN Cheng-Lin. Fault Diagnosis Based on Fusion and Updating of Diagnosis Evidence. ACTA AUTOMATICA SINICA, 2016, 42(1): 107-121. doi: 10.16383/j.aas.2016.c150403

基于诊断证据静态融合与动态更新的故障诊断方法

doi: 10.16383/j.aas.2016.c150403
基金项目: 

重庆市高等学校优秀人才支持计划 2014-18

国家自然科学基金 61374123, 61433001, 61573076

详细信息
    作者简介:

    张镇 杭州电子科技大学自动化学院硕士研究生.2013年获得河南科技大学电子信息工程学院学士学位.主要研究方向为证据融合,证据更新及智能故障诊断.E-mail:zhangzhen87@foxmail.com

    李世宝 杭州电子科技大学自动化学院硕士研究生.2014年获浙江师范大学行知学院电子信息工程学士学位.主要研究方向为证据推理,证据融合及故障诊断.E-mail:lishibaodove@163.com

    文成林 杭州电子科技大学教授,博士.主要研究方向为多尺度估计理论,多源信息融合技术,故障诊断与容错控制处理.E-mail:wencl@hdu.edu.cn

    通讯作者:

    XU Xiao-Bin Ph. D., associate professor at Hangzhou Dianzi University. His research interest covers fault diagnosis and reliability evaluation of systems, and intelligence information processing. Corre sponding author of this paper

Fault Diagnosis Based on Fusion and Updating of Diagnosis Evidence

Funds: 

Excellent Talent Support Program of Chongqing Higher School 2014-18

National Natural Science Foundation of China 61374123, 61433001, 61573076

More Information
    Author Bio:

    Master student at the School of Automation, Hangzhou Dianzi University. He received his bachelor degree from the College of Electronical and Information Engineering, Henan University of Science and Technology in 2013. His research interest covers evidence combination, evidence up dating, and intelligence fault diagnosis.

    Master student at the School of Automation, Hangzhou Di anzi University. He received his bache lor degree from the College of Electronical and Information Engineering, Zhejiang Normal University Xingzhi College in 2014. His research interest covers evidential reasoning, evidence combination, and fault diagnosis

    Ph. D., professor at Hangzhou Dianzi University. His research interest covers multi-scale estimation theory, multi-sensor information fusion technology, fault diagnostics, and tolerant control

  • 摘要: 提出一种将诊断证据静态融合与动态更新相结合的故障诊断方法.在静态融合阶段,利用Dempster组合规则融合每个时刻的多条局部诊断证据,获取静态融合证据,并给出基于证据距离的故障信度静态收敛指标;在动态更新阶段,基于条件化的线性组合更新规则,利用当前时刻静态融合证据更新历史证据,获取更新后的全局性诊断证据,并给出基于S函数的故障信度动态收敛指标.在两个阶段中,基于静态和动态信度收敛性指标函数,分别给出相应的优化学习方法,获取静态融合中局部诊断证据的静态折扣系数、动态更新中历史与当前证据的更新权重系数等参数的最优值.在最大信度原则下,利用更新后获取的诊断证据做出诊断决策.最后,通过在电机柔性转子实验台上的诊断实验,将所提方法与已有的典型融合诊断方法进行了对比分析,说明所提出的融合诊断方法及其性能指标函数和参数优化方法的有效性.
  • 图  1  故障诊断的静态融合与动态更新过程

    Fig.  1  Static fusion and dynamic updating processes of fault diagnosis

    图  2  距离 $d$ 与相似度 $Sim$ 的关系图( $a=6,8,15$ )

    Fig.  2  Relationship between $d$ and $Sim$ ( $a=6,8,15$ )

    图  3  ZHS-2电机柔性转子系统

    Fig.  3  ZHS-2 motor flexible rotor

    图  4  未优化和优化时静态融合与动态更新结果的比较

    Fig.  4  Static fusion and dynamic updating result comparisons between the non-optimization case and the optimization case

    表  1  相邻两全局证据相似度之差

    Table  1  The similarity difference between two adjacent global evidence

    状态: $m $ 12
    采样时刻 $t$ 1 2 3 4 5 6 7
    相似度差 $\Delta^l_{t,m}$ 1 0 0 0.8 0.1 -0.2 0.2
    渐消因子 $λ^l_{t,m}$ 1 1/2 1/3 1 1/2 1/3
    下载: 导出CSV

    表  2  未折扣和折扣优化后融合结果 ${{m}_{\oplus,t}}$ 和 $^{{\boldsymbol \alpha} }{{m}_{\oplus ,t}}$ 对应的 $SI_m$ 取值

    Table  2  The $SI_m$ values of the non-discounted fusion result ${{m}_{\oplus ,t}}$ and the discounted and optimized fusion result $^{{\boldsymbol \alpha} }{{m}_{\oplus ,t}}$

    融合方法 $SI_m$
    ${{m}_{\oplus ,t}}$ 23.1225
    $^{{\boldsymbol \alpha} }{{m}_{\oplus ,t}}$ 21.7505
    下载: 导出CSV

    表  3  未折扣融合结果 ${{m}_{\oplus ,t}}$ 的误报/漏报次数统计表

    Table  3  The statistics of alarms and no-alarms of the non-discounted fusion result ${{m}_{\oplus ,t}}$

    表  4  折扣优化后融合结果α ${{m}_{\oplus ,t}}$ 的误报/漏报次数的统计表

    Table  4  The statistics of alarms and no-alarms of the discounted & optimized fusion result α ${{m}_{\oplus ,t}}$

    表  5  未优化和优化后更新结果 $m_{1:t}$ 和 ${}^a m_{1:t}$ 相应的 $UDI$ 、 $DDI$ 和 $DI$ 取值

    Table  5  $UDI$ , $DDI$ and $DI$ values of the non-optimized updating result $m_{1:t}$ and the optimized updating result ${}^a m_{1:t}$

    更新方法 $UDI$ $DDI$ $DI$ $(\kappa =\eta =0.5) $
    $m_{1:t}$ 0.82850.8195 0.1760
    ${}^a m_{1:t}$ 0.9411 0.95850.0502
    下载: 导出CSV

    表  6  未优化的更新结果m1:t的误报/漏报次数统计表

    Table  6  The statistics of alarms and no-alarms of the non-optimized updating result m1:t

    表  7  优化后史新结果αm1:t的误报/漏报次数的统计表

    Table  7  The statistics of alarms and no-alarms of the optimized updating result αm1:t

    表  8  测试样本下所获 ${}^a m_{1:t}$ 的 $UDI$ 、 $DDI$ 和 $DI$ 取值

    Table  8  $UDI$ , $DDI$ and $UDI$ values of ${}^a m_{1:t}$ obtained from the test sample

    基于参数优化的更新结果 $UDI$ $DDI$ $DI$ $(\kappa =\eta =0.5) $
    ${}^a m_{1:t}$ 0.94340.9720 0.0423
    下载: 导出CSV

    表  9  测试样本下所获αm1:t的误报/漏报次数的统计表

    Table  9  The statistics of alarms and no-alarms of αm1:t obtained from the test sample

    表  10  基于无限惯性策略的线性更新方法的误报/漏报次数的统计表

    Table  10  The statistics of alarms and no-alarms based on the infinite inertia based updating strategy

    表  11  基于零惯性策略的线性更新方法的误报/漏报次数的统计表

    Table  11  The statistics of alarms and no-alarms based on the zero inertia based updating strategy

    表  12  基于比例性策略的线性更新方法的误报/漏报次数的统计表

    Table  12  The statistics of alarms and no-alarms based on the proportional inertia based updating strategy

    表  13  文献[1]中更新方法确诊率统计的误报/漏报次数的统计表

    Table  13  The statistics of alarms and no-alarms based on updating strategy in [1]

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  • 收稿日期:  2015-06-24
  • 录用日期:  2015-10-19
  • 刊出日期:  2016-01-01

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