Pipeline small leak detection based on virtual sample generation and unified feature extraction
Title
Pipeline small leak detection based on virtual sample generation and unified feature extraction
Subject
Extraction
Pipelines
Leak detection
Learning systems
Feature extraction
Description
Due to the lack of samples and concealed features, petroleum pipeline small leak detection is still a great challenge. In this paper, a method based on virtual sample generation (VSG) and unified feature extraction (UFE) techniques is proposed to detect small leak. First, an effective sample generation algorithm based on limited raw sample and prior knowledge is designed. We verify the effectiveness of the generated sample from sample diversity and statistical similarity. Then, seven statistical features and a group of symbol transformation features are extracted to deeply mine sample information. And then, the extracted features are combined to unified features (UFs). Finally, small leak detection models are obtained by training UFs using four machine learning methods. In the experimental process, the proposed method is compared with other state-of-the-art small leak detection methods. Experimental results show that the proposed method has a better ability in pipeline small leak detection. 2021
184
Creator
Zang, Dong
Liu, Jinhai
Qu, Fuming
Publisher
Measurement: Journal of the International Measurement Confederation
Date
2021
Type
journalArticle
Identifier
2632241
10.1016/j.measurement.2021.109960
URL
http://dx.doi.org/10.1016/j.measurement.2021.109960
Citation
Zang, Dong, Liu, Jinhai, and Qu, Fuming, “Pipeline small leak detection based on virtual sample generation and unified feature extraction,” Lamar University Midstream Center Research, accessed May 18, 2024, https://lumc.omeka.net/items/show/28273.