| 引用本文: | 胡旭光,马大中,孙秋野,王占山,李晓瑜.基于要素矩阵触发的管道异常分布式检测[J].控制理论与应用,2017,34(8):1035~1045.[点击复制] |
| HU Xu-guang,MA Da-zhong,SUN Qiu-ye,WANG Zhan-shan,Li Xiaoyu.Pipeline anomaly distributed detection based on element-matrix trigger mechanism[J].Control Theory and Technology,2017,34(8):1035~1045.[点击复制] |
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| 基于要素矩阵触发的管道异常分布式检测 |
| Pipeline anomaly distributed detection based on element-matrix trigger mechanism |
| 摘要点击 2333 全文点击 1475 投稿时间：2016-11-24 修订日期：2017-07-03 |
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| DOI编号 10.7641/CTA.2017.60893 |
| 2017,34(8):1035-1045 |
| 中文关键词 分布式检测 管道管网 故障检测 事件触发 |
| 英文关键词 distributed detection pipeline network fault detection event-triggered |
| 基金项目 国家自然科学基金重大项目(61627809), 国家自然科学基金项目(61473069, 61573094, 61773109), 中央高校基本科研业务费专项基金项目(N160 |
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| 中文摘要 |
| 针对管网管道数据传输频繁的问题, 为了能够对管网进行实时检测和减少站场间数据传输次数, 通过研究
管网站场内不同数据的类型及传输特点, 建立了管网管道的运行模型, 提出了基于要素矩阵触发的管道异常分布式
检测方法. 该分布式异常检测方法利用站场设备数据建立管道数据要素矩阵并通过提出的基于随机矩阵单环定理
的异常判断准则来决定站场间数据传输的时刻, 然后采用考虑管道倾角的数据衰减模型将相邻站场的压力数据进
行折算并且与实际管道压力差相比较, 得到管道的运行状态. 该方法采用要素矩阵触发和压力数据衰减模型对数
据进行传输和处理, 可以减少站场间的数据传输频率以及提高检测的精度. 最后通过管网站场历史数据验证了所提
方法的有效性. |
| 英文摘要 |
| In order to real-time detection and reduce the number of data transmission on pipeline network that deliver
a great deal of data frequently, a data model of station is established through the investigation on the station’s data type
and transmission and the method of pipeline anomaly distributed detection based on element-matrix trigger mechanism is
also proposed. The method uses station device data to build the pipeline element-matrix, and the time of data transmission
between tributary stations is decided by anomaly detection criteria based on random matrix’s single ring theorem. Then,
the pressure data of adjacent station is discounted by attenuation data model considering pipeline’s angle. And the pipeline
operating condition is gained by compared with the difference between real pressure data difference and model data difference.
Element-matrix trigger and attenuation data model are adopted in the method which reduce the frequency of data
transmission between station and improve detection precision. Finally, the effectiveness of the proposed method is verified
via simulation historical data processing. |
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