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一种基于数据可靠性和区间证据推理的故障检测方法

周志杰 刘涛源 胡冠宇 李思作 李改灵 贺维

周志杰, 刘涛源, 胡冠宇, 李思作, 李改灵, 贺维. 一种基于数据可靠性和区间证据推理的故障检测方法. 自动化学报, 2020, 46(12): 2628−2637 doi: 10.16383/j.aas.c180518
引用本文: 周志杰, 刘涛源, 胡冠宇, 李思作, 李改灵, 贺维. 一种基于数据可靠性和区间证据推理的故障检测方法. 自动化学报, 2020, 46(12): 2628−2637 doi: 10.16383/j.aas.c180518
Zhou Zhi-Jie, Liu Tao-Yuan, Hu Guan-Yu, Li Si-Zuo, Li Gai-Ling, He Wei. A fault detection method based on data reliability and interval evidence reasoning. Acta Automatica Sinica, 2020, 46(12): 2628−2637 doi: 10.16383/j.aas.c180518
Citation: Zhou Zhi-Jie, Liu Tao-Yuan, Hu Guan-Yu, Li Si-Zuo, Li Gai-Ling, He Wei. A fault detection method based on data reliability and interval evidence reasoning. Acta Automatica Sinica, 2020, 46(12): 2628−2637 doi: 10.16383/j.aas.c180518

一种基于数据可靠性和区间证据推理的故障检测方法

doi: 10.16383/j.aas.c180518
基金项目: 国家自然科学基金(61773388, 61751304, 61374138, 71601168) 资助
详细信息
    作者简介:

    周志杰:火箭军工程大学教授. 主要研究方向为故障诊断, 复杂系统建模. 本文通信作者.E-mail: zhouzj04@tsinghua.org.cn

    刘涛源:火箭军工程大学控制工程专业硕士研究生. 主要研究方向为状态监控, 故障检测, 报警阈值优化.E-mail: 15771717394@163.com

    胡冠宇:海南师范大学信息科学技术学院副教授. 主要研究方向为复杂系统建模与安全性评估, 证据推理与置信规则库建模方法.E-mail: huguanyu0708@163.com

    李思作:火箭军驻7111厂军事代表室工程师. 主要研究方向为伺服系统的研制、 生产及故障诊断.E-mail: redeastfan@gmail.com

    李改灵:火箭军工程大学控制科学与工程专业博士研究生. 主要研究方向为故障诊断, 多元信息融合技术.E-mail: senior568_lee@163.com

    贺维:黑龙江农业工程职业学院讲师. 主要研究方向为置信规则库和深度学习.E-mail: he_w_1980@163.com

A Fault Detection Method Based on Data Reliability and Interval Evidence Reasoning

Funds: Supported by National Natural Science Foundation of China (61773388, 61751304, 61374138, 71601168)
  • 摘要: 为解决故障检测方法在处理数据不确定性问题上的不足, 本文提出一种基于数据可靠性和区间证据推理(Interval evidential reasoning, IER)的故障检测方法. 该方法通过融合专家知识与考虑可靠性的监测数据, 实现报警阈值区间的更新与优化, 从而提高故障检测的准确性. 首先基于信息一致性方法计算数据可靠度, 然后基于区间证据推理理论, 构建区间阈值的更新与优化模型, 最后基于投影协方差矩阵自适应进化策略算法求解优化模型, 得到故障检测误漏报率最小的最优报警阈值区间. 对石油管道泄漏实例和航天继电器加速寿命测试实例的故障检测问题进行了研究, 通过对比分析, 验证了所提方法的有效性.
  • 图  1  基于数据可靠性和IER的故障检测方法结构图

    Fig.  1  Structure diagram of fault detection method based on data reliability and IER

    图  2  考虑数据可靠性的阈值更新模型

    Fig.  2  Threshold update model considering data reliability

    图  3  阈值区间与评价等级的关系

    Fig.  3  Relationship between threshold interval and evaluation level

    图  4  阈值优化模型结构图

    Fig.  4  Structure diagram of the threshold optimization model

    图  5  两类数据的故障检测

    Fig.  5  Fault detection for two types of data

    图  6  石油管道泄漏数据

    Fig.  6  Leakage data of oil pipelines

    图  7  JRC-7M继电器吸合时间数据图

    Fig.  7  Pull-in time data of JRC-7M relay

    表  1  初始阈值区间的区间置信度

    Table  1  Interval belief degree of initial threshold interval

    评价等级区间置信度
    $H_1$$\left[ {0,0.0214} \right]$
    $H_2$$\left[ {0.7538,1} \right]$
    $H_3$$\left[ {0,0.2462} \right]$
    下载: 导出CSV

    表  2  监测数据的区间置信度

    Table  2  Interval belief degree of monitoring data

    训练数据H1H2H3
    $x_1$$\left[ {0.8571,0.8571} \right]$$\left[ {0.1429,0.1429} \right]$$\left[ {0,0} \right]$
    $\vdots$$\vdots$$\vdots$$\vdots$
    $x_{300}$$\left[ {0,0} \right]$$\left[ {1,1} \right]$$\left[ {0,0} \right]$
    $\vdots$$\vdots$$\vdots$$\vdots$
    $x_{700}$$\left[ {0,0} \right]$$\left[ {0.9167,0.9167} \right]$$\left[ {0.0823,0.0823}\right]$
    下载: 导出CSV

    表  3  总体区间置信度更新过程

    Table  3  The update process of overall interval belief degree

    总体区间置信度$H_1$$H_2$$H_3$
    $\left[ {\min \beta _n ,\max \beta _n } \right]_1$$\left[ {0,0} \right]$$\left[ {0.7910,0.7963} \right]$$\left[ {0.2037,0.2090} \right]$
    $\vdots$$\vdots$$\vdots$$\vdots$
    $\left[ {\min \beta _n ,\max \beta _n } \right]_{300}$$\left[ {0,0} \right]$$\left[ {0.8751,0.8816} \right]$$\left[ {0.1184,0.1249} \right]$
    $\vdots$$\vdots$$\vdots$$\vdots$
    $\left[ {\min \beta _n ,\max \beta _n } \right]_{700}$$\left[ {0,0} \right]$$\left[ {0.9262,0.9507} \right]$$\left[ {0.0493,0.0738} \right]$
    下载: 导出CSV

    表  4  故障检测效果比较(%)

    Table  4  Comparison of fault detection effects (%)

    优化方法阈值误报率漏报率$G$
    未优化$\left[ {0.8925,1.0477} \right]$$0$$9$$9$
    神经网络方法$\left[ {0.9832,1.0990} \right]$$0$$12$$12$
    IER方法$\left[ {0.9510,0.9749} \right]$$0.6$$7.8$$8.4$
    IER$(r_i)$$\left[ {0.9296,0.9443} \right]$$4.8$$1.8$$6.6$
    下载: 导出CSV

    表  5  第1组数据的总体区间置信度更新过程

    Table  5  The overall interval confidence update process for the first set of data

    总体区间置信度$H_1$$H_2$$H_3$
    $\left[ {\min \beta _n ,\max \beta _n } \right]_1$$\left[ {0,0} \right]$$\left[ {0.7093,0.7619} \right]$$\left[ {0.2381,0.2907} \right]$
    $\vdots$$\vdots$$\vdots$$\vdots$
    $\left[ {\min \beta _n ,\max \beta _n } \right]_{100}$$\left[ {0,0} \right]$$\left[ {0.6842,0.7135} \right]$$\left[ {0.2865,0.3158} \right]$
    $\vdots$$\vdots$$\vdots$$\vdots$
    $\left[ {\min \beta _n ,\max \beta _n } \right]_{300}$$\left[ {0,0} \right]$$\left[ {0.6547,0.7488} \right]$$\left[ {0.2512,0.3453} \right]$
    下载: 导出CSV

    表  6  第2组总体区间置信度更新过程

    Table  6  The overall interval confidence update process for the second set of data

    总体区间置信度$H_1$$H_2$$H_3$
    $\left[ {\min \beta _n ,\max \beta _n } \right]_1$$\left[ {0,0} \right]$$\left[ {0.7093,0.7619} \right]$$\left[ {0.2381,0.2907} \right]$
    $\vdots$$\vdots$$\vdots$$\vdots$
    $\left[ {\min \beta _n ,\max \beta _n } \right]_{100}$$\left[ {0,0} \right]$$\left[ {0.6837,0.7156} \right]$$\left[ {0.2844,0.3163} \right]$
    $\vdots$$\vdots$$\vdots$$\vdots$
    $\left[ {\min \beta _n ,\max \beta _n } \right]_{280}$$\left[ {0,0} \right]$$\left[ {0.6466,0.7402} \right]$$\left[ {0.2598,0.3534} \right]$
    下载: 导出CSV
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出版历程
  • 收稿日期:  2018-07-28
  • 录用日期:  2018-12-03
  • 网络出版日期:  2020-12-29
  • 刊出日期:  2020-12-29

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