基于贝叶斯网络的测井车载数据中心设备故障诊断方法研究

doi:10.3969/j.issn.1002-302x.2023.01.008

石油科技论坛 ›› 2023, Vol. 42 ›› Issue (1): 53-60.DOI: 10.3969/j.issn.1002-302x.2023.01.008

基于贝叶斯网络的测井车载数据中心设备故障诊断方法研究

夏大坤¹　舒欢²　高凌云¹　吴德银¹　宫玉明¹　贺东洋¹　陈茂柯³

1.中国石油集团测井有限公司制造公司； 2.重庆大学机械与运载工程学院； 3.中国石油集团测井有限公司西南分公司

出版日期:2023-03-03 发布日期:2023-03-03
作者简介:夏大坤，1992年生，2015年本科毕业于长江大学测控技术与仪器专业，工程师，主要从事信息化系统设计工作。
基金资助:
中国石油集团测井有限公司制造公司科研项目“中油测井安全信息化设备运维系统研究”（编号：2021-04-11）。

Fault Diagnosis Method of Logging Onboard Data Center Equipment Based on Bayesian Network

Xia Dakun¹,Shu Huan²,Gao Lingyun¹,Wu Deyin¹,Gong Yuming¹,He Dongyang¹,Chen Maoke³

1. Manufacturing Company, CNPC Logging Co. Ltd., Xi’an 710000, China; 2. College of Mechanics and Vehicle Engineering, Chongqing University, Chongqing 400044, China; 3. Southwest Company, CNPC Logging Co. Ltd., Chongqing 400021, China

Online:2023-03-03 Published:2023-03-03

摘要/Abstract

摘要： 针对测井车载数据中心设备提出一种基于贝叶斯网络（BN）的故障诊断方法，并建立了故障诊断BN模型，实现了测井车载数据中心设备的智能化诊断。在建立BN模型过程中，采用基于粗糙集的知识约简方法对专家提出的故障节点进行简化，利用专家知识初步构建BN结构，使用K2学习算法对BN进行优化；同时结合专家经验知识，用EM算法对BN进行参数学习，得到测井车载数据中心设备故障诊断BN模型。运用BN模型进行故障案例推理，基于历史数据对BN模型进行验证，结果表明该模型诊断准确率较高，能够为测井车载数据中心设备故障的快速定位和精确诊断提供依据。

关键词: 测井车载数据中心, 设备故障诊断, 贝叶斯网络, 模型构建, 专家知识库

Abstract: The fault diagnosis method based on Bayesian network (BN) is proposed for the logging onboard data center equipment. Meanwhile, the BN model for fault diagnosis is established to allow the logging onboard data center equipment to came under intelligent diagnosis. In the process of building the BN model, the knowledge reduced and simplified method based on the rough set is adopted to simplify the failure nodes put forward by experts. The BN structure is initially constructed on the basis of experts’ knowledge, with the K2 learning algorithm used for BN optimization. Combined with the experience and knowledge of experts, the EM algorithm is used to learn the parameters for BN, thus acquiring the fault -diagnosed BN model of the logging onboard data center equipment. The BN model is used to diagnose the fault cases while the historical data are based to verify the BN model. The results indicates that the model has a higher correction rate of diagnosis and the ability to provide the basis for rapidly positioning and precisely diagnosing the fault of the logging onboard data center equipment.

中图分类号:

夏大坤　舒欢　高凌云　吴德银　宫玉明　贺东洋　陈茂柯. 基于贝叶斯网络的测井车载数据中心设备故障诊断方法研究[J]. 石油科技论坛, 2023, 42(1): 53-60.

Xia Dakun, Shu Huan, Gao Lingyun, Wu Deyin, Gong Yuming, He Dongyang, Chen Maoke. Fault Diagnosis Method of Logging Onboard Data Center Equipment Based on Bayesian Network[J]. Petroleum Science and Technology Forum, 2023, 42(1): 53-60.

参考文献

[1]　邱国宾. 油田测井车的发展[J]. 中国石油和化工标准与质量, 2012, 33(15): 251.
Qiu Guobin. Development of logging truck in oil field[J]. China Petroleum and Chemical Standard and Quality, 2012, 33 (15): 251.
[2]　范国华, 张庆禄, 张宇翔. 测井企业移动放射源在线监控系统需求分析[J]. 科技创新与应用, 2020(34): 49-50.
Fan Guohua, Zhang Qinglu, Zhang Yuxiang. Requirement analysis of on-line monitoring system for mobile radioactive sources in logging enterprises[J]. Technology Innovation and Application, 2020(34): 49-50.
[3]　丁伟. 石油测井车井上监测系统的设计与实现[J]. 石油天然气学报, 2012, 34(6): 100-103.
Ding Wei. Design and implementation of monitoring system on logging truck[J]. Journal of Oil and Gas Technology, 2012, 34 (6): 100-103.
[4]　石玉江, 刘国强, 钟吉彬, 等. 基于大数据的测井智能解释系统开发与应用[J]. 中国石油勘探, 2021, 26(2): 113-126.
Shi Yujiang, Liu Guoqiang, Zhong Jibin, et al. Development and application of intelligent logging interpretation system based on big data[J]. China Petroleum Exploration, 2021, 26(2): 113-126.
[5]　Lei Y, Dragan D. Diagnosis of intermittent connections for devicenet[J]. Chinese Journal of Mechanical Engineering, 2010, 23(5): 606-612.
[6]　王海瑛, 徐明, 宋奎博. 列控（CTCS3．300T）车载设备常见故障及处理[J]. 铁道通信信号, 2013, 49(9): 34-36．
Wang Haiying, Xu Ming, Song Kuibo. Common failures of CTCS3-300T on-board ATC equipment and their processing[J]. Railway Signalling & Communication, 2013, 49 (9): 34-36.
[7]　文成林, 吕菲亚, 包哲静, 等. 基于数据驱动的微小故障诊断方法综述[J]. 自动化学报, 2016, 42(9): 1285-1299．
Wen Chenglin, Lv Feiya, Bao Zhejing, et al. A review of data driven-based incipient fault diagnosis[J]. Acta Automatica Sinica, 2016, 42 (9): 1285-1299.
[8]　陈静, 蒋正凯, 付敬奇. 基于Netica的自学习贝叶斯网络的构建[J]. 电子测量与仪器学报, 2016,30(11): 1687-1693.
Chen Jing, Jiang Zhengkai, Fu Jingqi. Construction of self-learning Bayesian network based on Netica[J]. Journal of Electronic Measurement and Instrumentation, 2016, 30 (11): 1687-1693.
[9]　张振海, 王晓明, 党建武, 等. 基于专家知识融合的贝叶斯网络结构学习方法[J]. 计算机工程与应用, 2014(2): 1-4, 9.
Zhang Zhenhai, Wang Xiaoming, Dang Jianwu, et al. Bayesian network structure learning method based on expert knowledge fusion[J]. Computer Engineering and Applications, 2014(2): 1-4, 9.
[10] 沐守宽, 周伟. 缺失数据处理的期望-极大化算法与马尔可夫蒙特卡洛方法[J]. 心理科学进展, 2011,19(7): 1083-1090.
Mu Shoukuan, Zhou Wei. Handling missing data: Expectation-maximization algorithm and Markov Chain Monte Carlo algorithm[J]. Advances in Psychological Science, 2011, 19(7): 1083-1090．
[11] Pawlakb Z. Rough set approach to knowledge-based decision support[J]. European Journal of Operational Research, 1995, 99(1): 48-57．
[12] 李玉兰. 基于贝叶斯网络的列控车载设备故障诊断研究[D]. 北京: 北京交通大学, 2016.
Li Yulan. Research on fault diagnosis for on-board equipment of train control system based on Bayesian network[D]. Beijing: Beijing Jiaotong University, 2016.
[13] Cooper G F, Herskovits E. A Bayesian method for the induction of probabilistic networks from data[J]. Machine Learning, 1992, 9(4): 309-347.
[14] Renooij S, Witteman C. Talking probabilities: Communicating probabilistic information with words and numbers[J]. International Journal of Approximate Reasoning, 1999, 22(3): 169-194．
[15] 赵阳, 徐田华, 周玉平, 等. 基于贝叶斯网络的高铁信号系统车载设备故障诊断方法的研究[J]. 铁道学报, 2014, 36(11): 48-53.
Zhao Yang, Xu Tianhua, Zhou Yuping, et al. Bayesian network based fault diagnosis system for vehicle on-board equipment of high-speed railway[J]. Journal of the China Railway Society, 2014, 36 (11): 48-53.

基于贝叶斯网络的测井车载数据中心设备故障诊断方法研究

Fault Diagnosis Method of Logging Onboard Data Center Equipment Based on Bayesian Network

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