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

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...

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Bibliographic Details
Published inIEEE transactions on control systems technology Vol. 31; no. 1; pp. 426 - 433
Main Authors Amini, Iman, Jing, Yindi, Chen, Tongwen
Format Journal Article
LanguageEnglish
Published New York IEEE 01.01.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Adaptive filters
Alarm systems
data analysis
Density
Distribution functions
Energy sources
Estimation
fault detection
Flow velocity
Kernel
Kernels
Leak detection
Oils
Pipelines
Probability density function
process monitoring
signal processing
Steady-state
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Summary: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.
Bibliography:ObjectType-Article-1
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content type line 14
ISSN:1063-6536
1558-0865
DOI:10.1109/TCST.2022.3172524