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大数据结构化与数据驱动的复杂系统维修决策

韩中 程林 熊金泉 刘满君

韩中, 程林, 熊金泉, 刘满君. 大数据结构化与数据驱动的复杂系统维修决策. 自动化学报, 2020, 46(2): 385-396. doi: 10.16383/j.aas.c170638
引用本文: 韩中, 程林, 熊金泉, 刘满君. 大数据结构化与数据驱动的复杂系统维修决策. 自动化学报, 2020, 46(2): 385-396. doi: 10.16383/j.aas.c170638
HAN Zhong, CHENG Lin, XIONG Jin-Quan, LIU Man-Jun. Complex System Maintenance Decisions Based on Big Data Structuration and Data-driven. ACTA AUTOMATICA SINICA, 2020, 46(2): 385-396. doi: 10.16383/j.aas.c170638
Citation: HAN Zhong, CHENG Lin, XIONG Jin-Quan, LIU Man-Jun. Complex System Maintenance Decisions Based on Big Data Structuration and Data-driven. ACTA AUTOMATICA SINICA, 2020, 46(2): 385-396. doi: 10.16383/j.aas.c170638

大数据结构化与数据驱动的复杂系统维修决策

doi: 10.16383/j.aas.c170638
基金项目: 

国家自然科学基金 61562063

国家自然科学基金 51807107

陕西省教育厅专项基金 18JK0713

详细信息
    作者简介:

    韩中  博士后, 琼台师范学院信息科学技术系副教授.主要研究方向为复杂系统的可靠性安全性, 故障诊断, 人工智能, 大数据. E-mail: hanyaozhong@sina.com

    程林  清华大学电机系副教授.主要研究方向为电力系统可靠性分析, 电力系统稳定性分析与控制, 能源互联网规划评估分析. E-mail: chengli@mail.tsinghua. edu.cn

    刘满君  清华大学电机系研究员, 博士.主要研究方向为电力系统可靠性, 连锁故障分析. E-mail: liumanjun@mail.tsing-hua.edu.cn

    通讯作者:

    熊金泉  南昌师范学院数计系教授.主要研究方向为计算机图形图像处理及大数据应用.本文通信作者. E-mail: xjq8931@163.com

Complex System Maintenance Decisions Based on Big Data Structuration and Data-driven

Funds: 

National Natural Science Foundation of China 61562063

National Natural Science Foundation of China 51807107

the Special Scientific Research of Shaanxi Provincial Department of Education 18JK0713

More Information
    Author Bio:

    HAN Zhong  Post-doctoral, associate professor at the School of Information Science and Technology, Qiongtai Normal University. His research interest covers the reliability and the safety for complex systems, fault diagnosis, artificial intelligence and big data

    CHENG Lin  Associate professor in the Department of Electrical Engineering, Tsinghua University. His research interest covers reliability analysis of power systems, analysis and control of system stability, plan, evaluation and analysis of energy internets

    LIU Man-Jun  Ph. D., researcher in the Department of Electrical Engineering, Tsinghua University. His research interest covers the reliability of power systems, cascading failure analysis

    Corresponding author: XIONG Jin-Quan  Professor at the School of Mathematics and Computer, Nanchang Normal University. His research interest covers image processing and big data application. Corresponding author of this paper
  • 摘要: 现代大型机电系统组成结构越来越复杂、智能化程度越来越高, 然而系统维修工作却越来越困难; 另外, 尽管快速发展的信息技术使得系统内部的各种流数据得到了有效的保存, 但却缺乏对这类大数据的有效利用、实现复杂系统的维修控制与决策.为此, 提出了大数据结构化与数据驱动的复杂系统维修决策方法.大数据结构化使用了层次分析法(Analytic hierarchy process, AHP)的思想, 依次建立系统维修的各个层级模型; 基于模型抽象出支持系统维修的数据变量、提炼出各层级变量的表达函数; 研究进一步实现了维护决策的数据驱动技术, 在模型和函数之上定义了数据状态块矩阵, 通过设计矩阵的特殊运算算法完成维修决策的数据驱动.最后, 使用一个具体的例子来说明提出方法的可用性, 结果证明提出的方法是可行的, 符合设备维修决策建设目标, 即维修方法经济、高效与实用.
    Recommended by Associate Editor WANG Zhuo
    1)  本文责任编委 王卓
  • 图  1  维修决策系统模型A

    Fig.  1  A maintenance decision system model A

    图  2  维修模型A-1

    Fig.  2  Maintenance model A-1

    图  3  维修方案决策模型A-2

    Fig.  3  Maintenance solution decision model A-2

    图  4  某生产系统设备连接示意图

    Fig.  4  A production system equipment joint diagram

    图  5  某机组异常状态图

    Fig.  5  An abnormal state diagram for a set equipment

    图  6  常见的FFT信号转换

    Fig.  6  A FFT signal conversion diagram

    图  7  系统优化线路图

    Fig.  7  A system optimization route diagram

    表  1  某设备群现场数据

    Table  1  A field data of equipment groups

    油压(MPa) 温度(℃) 气压(Kpa) 气流(kNm3/h) 液位(%) 汽压(Mpa) 箱振动(µm) 气温(℃) 气压(Mpa) 转速(rpm)
    0.2125763 29.54823 95.59524 129.4939 46.18437 10.03053 20.73275 13.55311 0.5045177 11 182.2
    0.2124542 29.60927 95.59524 129.2410 46.00122 10.03663 19.95911 13.73626 0.5045177 11 182.2
    0.2128205 29.54823 95.59524 129.4476 46.18437 10.03053 19.95911 13.55311 0.5045177 11 182.2
    0.2126984 29.54823 95.59524 129.5999 46.21490 10.03663 20.15137 13.55311 0.5045177 11 182.2
    0.2126984 29.54823 95.59524 129.6462 46.15385 10.03663 20.10559 13.55311 0.5045177 11 182.2
    0.2124542 29.60927 95.59524 129.1827 46.18437 10.03053 20.39399 13.73626 0.5045177 11 182.9
    0.2123321 29.54823 95.59524 129.9566 46.21490 10.02442 20.00946 13.55311 0.5047619 11 182.9
    0.2125763 29.54823 95.59524 130.0745 46.27595 10.02442 19.56999 13.55311 0.5045177 11 184.3
    0.2122100 29.54823 95.55556 129.5866 46.27595 10.02442 19.71648 13.73626 0.5045177 11 184.3
    0.2125763 29.54823 95.55556 129.7047 46.27595 10.02442 20.54048 13.55311 0.5045177 11 184.3
    0.2126984 29.54823 95.59524 129.6329 46.33700 10.01832 19.90875 13.73626 0.5042735 11 183.6
    0.21221 29.54823 95.55556 129.9104 46.39805 10.01221 19.90875 13.55311 0.5045177 11 185.0
    下载: 导出CSV

    表  2  测点设备的流量数据

    Table  2  The flux datum of measuring points

    f(Ti+1) f(Ti+2) f(Ti+3) f(Ti+4) f(Ti+5) f(Ti+6) f(Ti+7) f(Ti+8) f(Ti+9) f(Ti+10)
    131.4101 131.5507 131.3375 131.6185 132.0129 133.0345 131.6311 132.4577 132.7883 132.7623
    132.6695 131.5038 132.1837 131.4313 131.8313 132.5042 132.2923 131.354 132.0667 131.9528
    131.868 58.93486 0 0 0 0 0 0 0 0
    下载: 导出CSV

    表  3  测点设备的转速数据

    Table  3  The speed datum of measuring points

    s(Ti+1) s(Ti+2) s(Ti+3) s(Ti+4) s(Ti+5) s(Ti+6) s(Ti+7) s(Ti+8) s(Ti+9) s(Ti+10)
    11 159.33 11 157.25 11 156.56 11 157.95 11 162.79 11 159.33 11 156.56 11 155.87 11 156.56 11 155.87
    11 163.48 11 161.41 7 232.401 1 966.924 625.5474 75.64297 0 0 0 0
    0 33.64624 33.81691 33.98817 0 0 0 0 0 0
    下载: 导出CSV
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  • 收稿日期:  2017-12-01
  • 录用日期:  2018-05-30
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