Citation

Zang D, Liu J, Qu F. Measurement 2021; 184: e109960.

Copyright

(Copyright © 2021, Elsevier Publishing)

DOI

10.1016/j.measurement.2021.109960

PMID

unavailable

Abstract

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 Elsevier Publishing

Keywords: Pipeline transportation

Language: en

Keywords

Feature extraction; Pipelines; Leak detection; Extraction; Learning systems