Adaptive Naive Bayes Classifier Based Filter Using Kernel Density Estimation for Pipeline Leakage Detection

被引：1

作者：

Amini, Iman ^{[1
]}

Jing, Yindi ^{[1
]}

Chen, Tongwen ^{[1
]}

机构：

[1] Univ Alberta, Dept Elect & Comp Engn, Edmonton, AB T6G 2R3, Canada

来源：

IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY | 2023年 / 31卷 / 01期

基金：

加拿大自然科学与工程研究理事会;

关键词：

Pipelines; Steady-state; Estimation; Kernel; Probability density function; Alarm systems; Oils; data analysis; fault detection; leak detection; process monitoring; signal processing; UNIVARIATE ALARM SYSTEMS; PERFORMANCE ASSESSMENT; DESIGN;

D O I：

10.1109/TCST.2022.3172524

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Pipelines are among the principal means of transporting hydrocarbon fluids and gases, so leakage detection is critical to avoid the loss of these energy resources. In this article, based on studies in field data, we model pipeline leakage as an increase in the mean value of the flow rate difference between the inlet and the outlet sensors, where the increased value is unknown and subject to change. Then, an adaptive filter is proposed based on the naive Bayes classification and the estimated cumulative distribution function (CDF) of the data in steady-state conditions using kernel density estimation. The proposed filter has a better performance in small leaks in comparison with different benchmarks.

引用

页码：426 / 433

页数：8

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