基于MWSPCA-CBR的智能预警方法研究及其在石化工业中的应用

耿志强; 景邵星; 白菊; 王仲凯; 朱群雄; 韩永明

doi:10.11949/j.issn.0438-1157.20181340

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基于MWSPCA-CBR的智能预警方法研究及其在石化工业中的应用

过程系统工程 | 更新时间：2026-07-24

- 基于MWSPCA-CBR的智能预警方法研究及其在石化工业中的应用
- Improved intelligent warning method based on MWSPCA-CBR and its application in petrochemical industries
- 化工学报 2019年70卷第2期页码：572-580
- 作者机构：
  
  1.北京化工大学信息科学与技术学院，北京 100029
  2.智能过程系统工程教育部工程研究中心，北京 100029
- 作者简介：
  
  耿志强（1973—），男，博士，教授，gengzhiqiang@mail.buct.edu.cn
  韩永明（1987—），男，博士，副教授，hanym@mail.buct.edu.cn
- 基金信息：
  
  国家自然科学基金项目(61673046;61374166);国家重点研发计划项目(2018YFB0803501);中央高校基本科研业务费专项资金(XK1802-4)
- DOI：10.11949/j.issn.0438-1157.20181340
  中图分类号： TP 29
- 收稿：2018-11-15，
  
  修回：2018-11-22，
  
  网络首发：2020-06-16，
  
  纸质出版：2019-02-05
- 稿件说明：
移动端阅览
耿志强, 景邵星, 白菊, 王仲凯, 朱群雄, 韩永明. 基于MWSPCA-CBR的智能预警方法研究及其在石化工业中的应用[J]. 化工学报, 2019, 70(2): 572-580

GENG Zhiqiang, JING Shaoxing, BAI Ju, WANG Zhongkai, ZHU Qunxiong, HAN Yongming. Improved intelligent warning method based on MWSPCA-CBR and its application in petrochemical industries[J]. CIESC Journal, 2019, 70(2): 572-580
耿志强, 景邵星, 白菊, 王仲凯, 朱群雄, 韩永明. 基于MWSPCA-CBR的智能预警方法研究及其在石化工业中的应用[J]. 化工学报, 2019, 70(2): 572-580 DOI： 10.11949/j.issn.0438-1157.20181340.

GENG Zhiqiang, JING Shaoxing, BAI Ju, WANG Zhongkai, ZHU Qunxiong, HAN Yongming. Improved intelligent warning method based on MWSPCA-CBR and its application in petrochemical industries[J]. CIESC Journal, 2019, 70(2): 572-580 DOI： 10.11949/j.issn.0438-1157.20181340.

摘要

石油钻井是一项高风险性、耗资巨大的系统工程。为了智能预警石油钻井过程中的异常，缩短非生产时间，降低相关风险，提出一种基于移动窗稀疏主元分析法（MWSPCA）的案例推理（CBR）异常智能预警方法（MWSPCA-CBR）。首先利用MWSPCA算法分析钻井过程中的实时数据，快速定位出异常可能发生的时间，然后使用基于案例推理方法分析异常数据，确定可能的异常类型，并为实时监控专家提供相关异常的处理方法。所提方法应用到石油钻井过程异常预警中，实验结果验证了所提方法的可行性和有效性，为钻井过程降低风险成本提供了新思路。

Abstract

The petroleum drilling project is a high-risk and costly system project. To effectively scan for potential problems of drilling

reduce non-productive time and lower related risks

this paper proposes an improved intelligent warning method based on moving window sparse principal component analysis (MWSPCA) integrating case-based reasoning (CBR) (MWSPCA-CBR). First

the MWSPCA is used to analyze the real-time data in the drilling process

and the time of occurrence of the anomaly is quickly located. Then the abnormal data is analyzed by using the CBR method to give possible exception types

and the associated handling methods are provided for monitoring experts. Finally

the proposed method is applied to intelligent warn abnormal problems of the petroleum drilling

the experimental results verify the feasibility and effectiveness of the proposed method and provide new ideas for reducing risks and costs during the petroleum drilling process.

关键词

Keywords

references

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