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[1]李丹,张文奇,王寿喜,等.基于SPC-RF的天然气管道运行单变量故障预警方法[J].油气储运,2022,41(11):1341-1348.[doi:10.6047/j.issn.1000-8241.2022.11.014]
LI Dan,ZHANG Wenqi,WANG Shouxi,et al.Single variable fault warning method for gas pipelines during operation based on SPC-RF[J].Oil & Gas Storage and Transportation,2022,41(11):1341-1348.[doi:10.6047/j.issn.1000-8241.2022.11.014]
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LI Dan,ZHANG Wenqi,WANG Shouxi,et al.Single variable fault warning method for gas pipelines during operation based on SPC-RF[J].Oil & Gas Storage and Transportation,2022,41(11):1341-1348.[doi:10.6047/j.issn.1000-8241.2022.11.014]
基于SPC-RF的天然气管道运行单变量故障预警方法
《油气储运》[ISSN:1000-8241/CN:13-1093/TE]
卷:
41
期数:
2022年11期
页码:
1341-1348
栏目:
运行与管理
出版日期:
2022-11-25
- Title:
- Single variable fault warning method for gas pipelines during operation based on SPC-RF
- 文章编号:
- 1000-8241（2022）11-1341-08
- Keywords:
- gas pipelines; fault warning; control chart theory; random forest algorithm; pattern recognition
- 分类号:
- TE88
- 文献标志码:
- A
- 摘要:
- 天然气管网系统庞大、结构复杂，运行过程中故障频发，管道现有监测控制系统报警形式较为单一，尚不能在系统故障潜发期提前预警，信息化、智能化程度不足。为此，建立了基于控制图理论与随机森林算法的管道运行数据状态识别模型：将天然气管道运行数据与控制图理论相结合，根据管道故障参数的6种模式类型，建立了相应的控制图模型；运用随机森林算法，实现对数据不同模式的高精度智能识别。现场实例应用结果表明：基于控制图理论与随机森林算法的天然气管道运行数据状态识别模型具有高准确率、耗时短的优点，能够识别管道运行数据的实时状态，从而准确发出预警。新建模型适用于天然气管道运行中单变量故障预警，能够为管道安全运行提供技术保障。（图9，表1，参23）
- Abstract:
- Natural gas pipeline network system has a large and complex structure, with faults occurring frequently during the operation. However, existing monitoring and control system of pipelines is equipped with a single form of alarm, unable to give timely warning in the potential occurrence period of system faults. Besides, it is not fully informationized and intellectualized. Therefore, a state recognition model of pipeline production data based on the control chart theory and random forest algorithm was established. Specifically, a control chart model was built by combining the operation data of natural gas pipeline and the control chart theory based on the 6 types of model on pipeline fault parameters. Then, the high-precision intelligent recognition for different data models was realized with the random forest algorithm. The results of application on site show that the operation data status recognition model based on the control chart theory and random forest algorithm has high accuracy and short time consuming, capable of recognizing the real-time status of operation data and thus providing accurate warning. Generally, the new model is applicable to the single variable fault warning of natural gas pipeline during operation and could provide technical support to the safe operation of pipelines. (9 Figures, 1 Table, 23 References)
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备注/Memo
李丹，女，1996年生，助理工程师，2021年硕士毕业于西安石油大学油气储运工程专业，现主要从事海底管道流动安全保障方向的研究工作。地址：北京市朝阳区太阳宫南街6号院中海油大厦，100028。电话：18706862209。Email：539222902@qq.com通信作者：王寿喜，男，1962年生，教授，1998年博士毕业于西南石油大学油气田开发工程专业，现主要从事油气管网仿真、管道泄漏检测及油气集输工艺的研究工作。地址：陕西省西安市雁塔区电子二路18号，710065。电话：13679112809。Email：wangsx@yahoo.com
基金项目：中国石油化工股份有限公司科研攻关项目“基于长输天然气管道在线仿真的智能诊断技术研究与开发”，318019-7。
（收稿日期：2021-09-02；修回日期：2022-05-24；编辑：李在蓉）
更新日期/Last Update:
2022-11-25