Volume 48 Issue 9
Sep.  2022
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Article Navigation >  Journal of Mine Automation  > 2022  >  48(9): 77-83
SHI Xiaojuan, YAO Bing, GU Huabei. Fault diagnosis of mine drainage system based on fuzzy Bayesian network[J]. Journal of Mine Automation，2022，48(9)：77-84.  doi: 10.13272/j.issn.1671-251x.18014
Citation: SHI Xiaojuan, YAO Bing, GU Huabei. Fault diagnosis of mine drainage system based on fuzzy Bayesian network[J]. Journal of Mine Automation，2022，48(9)：77-84.  doi: 10.13272/j.issn.1671-251x.18014
shu

Fault diagnosis of mine drainage system based on fuzzy Bayesian network

doi: 10.13272/j.issn.1671-251x.18014

  • College of Mechanical Engineering, Xi'an University of Science and Technology, Xi'an 710054, China

  • Received Date: 2022-08-12
  • Rev Recd Date: 2022-09-06
    • Available Online: 2022-09-19
    Abstract
  • The mine drainage system is developing towards automation and intelligence. The system's structure and function are becoming more and more complex, and the abnormal function and failure of a single component may cause the failure of the whole system. The existing fault diagnosis methods of the mine drainage system have problems, such as difficult implementation, no consideration of the integrity of the system, and low fault diagnosis efficiency. In order to solve the above problems, a fault diagnosis method of mine drainage system based on fuzzy Bayesian network is proposed. Firstly, the fault tree analysis method is used to decompose the fault causes of the system layer by layer, and find out the root cause of the system fault. Secondly, the events in the fault tree are transformed into the nodes of the Bayesian network. The logic gates are transformed into the directed edges and conditional probabilities of the Bayesian network. The Bayesian network is constructed according to the mapping relationship between the fault tree and the Bayesian network. Thirdly, the fuzzy set theory is introduced into the Bayesian network. The correlation strength between fault and symptom is determined by expert evaluation. After fuzzification by triangular fuzzy number, averaging and defuzzification, the conditional probability of the fuzzy Bayesian network is obtained. Finally, according to the prior probability and conditional probability, the fuzzy Bayesian network is used to judge the probability of each root node fault. The simulation software Genie3.0 is used to establish the fuzzy Bayesian network, and the reasoning analysis and diagnosis test are carried out. The results show that the diagnosis accuracy of the method for each fault symptom is above 80%, and the average accuracy is 82.7%. The method can not only determine the specific position and specific components of the fault source, but also find out the weak nodes of the mine drainage system, eliminate potential faults, and improve the reliability and safety of the system.

     

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    • References(23)
  • [1]
    MEEGODA J N,JULIANO T M,POTTS L,et al. Implementation of a drainage information,analysis and management system[J]. Journal of Traffic and Transportation Engineering,2017,4(2):165-177.
    [2]
    MAJDI A,AMINI M,NASAB S K. Adequate drainage system design for heap leaching structures[J]. Journal of Hazardous Materials,2007,147(1/2):288-296.
    [3]
    李海军, 马登武, 刘霄, 等. 贝叶斯网络理论在装备故障诊断中的应用[M]. 北京: 国防工业出版社, 2009.

    LI Haijun, MA Dengwu, LIU Xiao, et al. Application of Bayesian network theory in equipment fault diagnosis[M]. Beijing: National Defense Industry Press, 2009.
    [4]
    张伟元,张朋飞,潘越,等. 基于正压给水的矿井自动排水控制系统[J]. 煤矿安全,2020,51(2):128-131. doi: 10.13347/j.cnki.mkaq.2020.02.029

    ZHANG Weiyuan,ZHANG Pengfei,PAN Yue,et al. Automatic drainage control system of coal mine based on positive pressure water supply[J]. Safety in Coal Mines,2020,51(2):128-131. doi: 10.13347/j.cnki.mkaq.2020.02.029
    [5]
    董铮. 机械故障诊断基础研究“何去何从”[J]. 科技创新导报,2017,14(32):21,23. doi: 10.16660/j.cnki.1674-098X.2017.32.021

    DONG Zheng. "Where to go" for basic research on mechanical fault diagnosis[J]. Science and Technology Innovation Herald,2017,14(32):21,23. doi: 10.16660/j.cnki.1674-098X.2017.32.021
    [6]
    魏晶. 具有故障自诊断功能的矿井节能排水控制系统[J]. 煤矿机电,2019,40(6):104-106. doi: 10.16545/j.cnki.cmet.2019.06.030

    WEI Jing. Energy-saving and drainage control system of mine with fault self-diagnosis function[J]. Colliery Mechanical & Electrical Technology,2019,40(6):104-106. doi: 10.16545/j.cnki.cmet.2019.06.030
    [7]
    赵丽娜,赵倩,宋保健. 基于Petri网的矿井排水系统多源信息故障诊断[J]. 电子技术,2018,47(5):73-76,72. doi: 10.3969/j.issn.1000-0755.2018.05.022

    ZHAO Lina,ZHAO Qian,SONG Baojian. Fault diagnosis of multi-source information of mine drainage system based on Petri net[J]. Electronic Technology,2018,47(5):73-76,72. doi: 10.3969/j.issn.1000-0755.2018.05.022
    [8]
    黄倩,黄强,鲁远祥,等. 基于LabVIEW的矿井排水装置故障监测系统的设计[J]. 工矿自动化,2011,37(5):8-11.

    HUANG Qian,HUANG Qiang,LU Yuanxiang,et al. Design of fault monitoring system of mine drainage device based on LabVIEW[J]. Industry and Mine Automation,2011,37(5):8-11.
    [9]
    张志强,王嫣. 矿井排水计算机控制及故障诊断系统开发[J]. 煤炭技术,2014,33(1):59-61. doi: 10.13301/j.cnki.ct.2014.01.053

    ZHANG Zhiqiang,WANG Yan. Development of computer control and fault diagnosis system in coal mine drainage[J]. Coal Technology,2014,33(1):59-61. doi: 10.13301/j.cnki.ct.2014.01.053
    [10]
    张海峰. 矿井自动排水系统故障诊断技术研究与实现[D]. 北京: 煤炭科学研究总院, 2016.

    ZHANG Haifeng. Research and implementation on fault diagnosis for mine automatic drainage system [D]. Beijing: China Coal Research Institute, 2016.
    [11]
    CAI Baoping,HUANG Lei,XIE Min. Bayesian networks in fault diagnosis[J]. IEEE Transactions on Industrial Informatics,2017,13(5):2227-2240. doi: 10.1109/TII.2017.2695583
    [12]
    张梅,许桃,孙辉煌,等. 基于模糊故障树和贝叶斯网络的矿井提升机故障诊断[J]. 工矿自动化,2020,46(11):1-5,45. doi: 10.13272/j.issn.1671-251x.17562

    ZHANG Mei,XU Tao,SUN Huihuang,et al. Fault diagnosis of mine hoist based on fuzzy fault tree and Bayesian network[J]. Industry and Mine Automation,2020,46(11):1-5,45. doi: 10.13272/j.issn.1671-251x.17562
    [13]
    陈振阳,刘成刚,钱阳阳,等. 基于故障树分析的排风热回收空调系统故障诊断与应用[J]. 苏州科技大学学报(工程技术版),2019,32(1):19-22. doi: 10.3969/j.issn.1672-0679.2019.01.004

    CHEN Zhenyang,LIU Chenggang,QIAN Yangyang,et al. Application of fault diagnosis based on fault tree analysis for exhaust heat recovery air-conditioning system[J]. Journal of Suzhou University of Science and Technology(Engineering and Technology),2019,32(1):19-22. doi: 10.3969/j.issn.1672-0679.2019.01.004
    [14]
    CHEN Ruwen, ZHU Songqing, HAO Fei, et al. Railway vehicle door fault diagnosis method with Bayesian network[C]. 2019 4th International Conference on Control and Robotics Engineering, Nanjing, 2019.
    [15]
    JUN H-B,KIM D. A Bayesian network-based approach for fault analysis[J]. Express Systems with Applications,2017,81:332-348. doi: 10.1016/j.eswa.2017.03.056
    [16]
    陈洪转,赵爱佳,李腾蛟,等. 基于故障树的复杂装备模糊贝叶斯网络推理故障诊断[J]. 系统工程与电子技术,2021,43(5):1248-1261. doi: 10.12305/j.issn.1001-506X.2021.05.12

    CHEN Hongzhuan,ZHAO Aijia,LI Tengjiao,et al. Fuzzy Bayesian network inference fault diagnosis of complex equipment based on fault tree[J]. Systems Engineering and Electronics,2021,43(5):1248-1261. doi: 10.12305/j.issn.1001-506X.2021.05.12
    [17]
    ZHANG Guohua,CHEN Wu,JIAO Yuyong,et al. A failure probability evaluation method for collapse of drill-and-blast tunnels based on multistate fuzzy Bayesian network[J]. Engineering Geology,2020,276:105752. doi: 10.1016/j.enggeo.2020.105752
    [18]
    叶银芳,李登峰,余高锋. 需求为三角模糊数的联合订货模型及其成本分摊方法[J]. 系统科学与数学,2019,39(7):1142-1158. doi: 10.12341/jssms13669

    YE Yinfang,LI Dengfeng,YU Gaofeng. A joint replenishment model with demands represented by triangular fuzzy numbers and its cost allocation method[J]. Journal of Systems Science and Mathematical Sciences,2019,39(7):1142-1158. doi: 10.12341/jssms13669
    [19]
    徐双蝶,张焰,苏运. 考虑不确定性变量模糊相关性的智能配电网概率潮流计算[J]. 电网技术,2020,44(4):1488-1500. doi: 10.13335/j.1000-3673.pst.2019.0423

    XU Shuangdie,ZHANG Yan,SU Yun. Probabilistic power flow calculation in smart distribution networks considering fuzzy correlation between uncertainty variables[J]. Power System Technology,2020,44(4):1488-1500. doi: 10.13335/j.1000-3673.pst.2019.0423
    [20]
    关茹男. 基于语言型Z−Numbers的多属性决策研究[D]. 太原: 山西大学, 2021.

    GUAN Runan. Research on multi-attribute decision making based on linguistic Z-Numbers[D]. Taiyuan: Shanxi University, 2021.
    [21]
    曾强,黄政,魏曙寰. 基于模糊理论和贝叶斯网络的燃气轮机健康状态评估方法[J]. 科学技术与工程,2020,20(11):4363-4369. doi: 10.3969/j.issn.1671-1815.2020.11.023

    ZENG Qiang,HUANG Zheng,WEI Shuhuan. Assessment method of gas turbine health based on fuzzy theory and Bayesian network[J]. Science Technology and Engineering,2020,20(11):4363-4369. doi: 10.3969/j.issn.1671-1815.2020.11.023
    [22]
    王金鑫,王忠巍,马修真,等. 基于贝叶斯网络的柴油机润滑系统多故障诊断[J]. 控制与决策,2019,34(6):1187-1194.

    WANG Jinxin,WANG Zhongwei,MA Xiuzhen,et al. Diagnosis of multiple faults of diesel engine lubrication system based on Bayesian networks[J]. Control and Decision,2019,34(6):1187-1194.
    [23]
    HUANG Yingping,MCMURRAN R,DHADYALLA G,et al. Probability based vehicle fault diagnosis:Bayesian network method[J]. Journal of Intelligent Manufacturing,2008,19(3):301-311. doi: 10.1007/s10845-008-0083-7
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