Machine Learning Model Comparison for Leak Detection in Noisy Industrial Pipelines

Kampelopoulos, Dimitrios; Papastavrou, George N.; Kousiopoulos, George P.; Karagiorgos, Nikolaos; Goudos, Sotirios K.; Nikolaidis, S.

doi:10.1109/mocast49295.2020.9200261

Cited by 7 publications

(2 citation statements)

References 13 publications

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“…Kampelopoulos et al [31] used various ML algorithms for monitoring system to detect potential leaks based on variances from a pipe's normal operation noise. They used a data set that had pipe noise measurements from typical operating conditions and artificial leak measurements.…”

Section: Intelligent-based Techniquesmentioning

confidence: 99%

Oil and Gas Pipelines Leakage Detection Approaches: A Systematic Review of Literature

Aljameel,

Alabbad,

Alomari

et al. 2024

IJSSE

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Section: Intelligent-based Techniquesmentioning

confidence: 99%

Oil and Gas Pipelines Leakage Detection Approaches: A Systematic Review of Literature

Aljameel,

Alabbad,

Alomari

et al. 2024

IJSSE

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“…In the network, the feature set of peak, average, and peak frequency achieved an accuracy of 97.2%, and the feature set of average and peak frequency achieved an accuracy of 96.9%. Kampelopoulos et al [16] emphasized that the role of the recall rate rather than accuracy is an important issue in evaluating the results of ML models in practical applications. In Tariq's research [17], the AdaBoost model was used to classify the leakage of metal and non-metal pipes, and the overall accuracy for metal pipes was 100%, and that of non-metal pipes was 94.93%.…”

Section: Introductionmentioning

confidence: 99%

A Tree-Based Machine Learning Method for Pipeline Leakage Detection

Shen

Cheng

2022

Water

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Leak detection techniques based on Machine Learning (ML) models can assist or even replace manual work in leak detection operations in water distribution systems (WDSs). However, studies on leakage detection based on on-site leak signals are limited compared to studies on lab-scale leak detection. The on-site leak signals have stronger interference and randomness, while leak signals in the laboratory are relatively simpler. To better assist on-site leak detection operations, the present paper develops and compares three ML-based models. For this purpose, many on-site tests were carried out, and tens of thousands of sets of on-site leak detection signals were collected. More than 6000 sets of these signals were marked and the signal features were extracted and analyzed from a statistical point of view. It was found that features such as the main frequency, the spectral roll-off rate, the spectral flatness, and one-dimensional (1-D) Mel Frequency Cepstrum Coefficient (MFCC) could well distinguish the leakage signals from non-leakage signals. After training the decision tree model, the performances of the random forest and Adaboost models were thoroughly compared. It was found that the false positive rates of the three models were 9.80%, 8.27% and 7.35%, all lower than 10%. In particular, the Adaboost model had the lowest false positive rate of 7.35%. The recall rate of the random forest and Adaboost models were 100% and 99.52%.

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Review on automated condition assessment of pipelines with machine learning

Liu

Bao

2022

Advanced Engineering Informatics

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Machine Learning Model Comparison for Leak Detection in Noisy Industrial Pipelines

Cited by 7 publications

References 13 publications

Oil and Gas Pipelines Leakage Detection Approaches: A Systematic Review of Literature

Oil and Gas Pipelines Leakage Detection Approaches: A Systematic Review of Literature

A Tree-Based Machine Learning Method for Pipeline Leakage Detection

Review on automated condition assessment of pipelines with machine learning

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