Application of Novel SN-1DCNN-LSTM framework in small sample oil and gas pipeline leakage detection
In this article, a novel ensemble framework of improved siamese network (SN) is proposed to address the small sample issue that deep learning approaches encounter, as well as to enhance the precision of pipeline leakage detection (PLD) under small sample conditions. Firstly, training samples are inp...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-03-01
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| Series: | Franklin Open |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2773186324000045 |
| _version_ | 1797202988166545408 |
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| author | Hongyu Gao Fenghua Hao Yiwen Zhang Xueyan Song Nan Hou |
| author_facet | Hongyu Gao Fenghua Hao Yiwen Zhang Xueyan Song Nan Hou |
| author_sort | Hongyu Gao |
| collection | DOAJ |
| description | In this article, a novel ensemble framework of improved siamese network (SN) is proposed to address the small sample issue that deep learning approaches encounter, as well as to enhance the precision of pipeline leakage detection (PLD) under small sample conditions. Firstly, training samples are input in pairs to the feature extraction network, and a combination of one-dimensional convolution neural network (1DCNN) and long short-term memory (LSTM) network is introduced to extract features of the time-series data, thus enhancing the effectiveness and robustness of feature extraction. Then, an improved relational metric network is designed to measure the similarity of features, to further strengthen the discriminative nature of the whole framework. In addition, the framework has been augmented with a classification network that can be used directly for PLD. The proposed SN-1DCNN-LSTM framework not only increases the number of training samples from the side, but also fully exploits the similarity information and data features between samples, overcoming the problem of instability and overfitting of deep learning models under small sample conditions. Finally, the experimental results verify the validity and superiority of the method under small sample conditions. |
| first_indexed | 2024-03-08T09:28:13Z |
| format | Article |
| id | doaj.art-c46d5961c127489482f9e853cfd6ac7d |
| institution | Directory Open Access Journal |
| issn | 2773-1863 |
| language | English |
| last_indexed | 2024-04-24T08:12:11Z |
| publishDate | 2024-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Franklin Open |
| spelling | doaj.art-c46d5961c127489482f9e853cfd6ac7d2024-04-17T04:50:31ZengElsevierFranklin Open2773-18632024-03-016100073Application of Novel SN-1DCNN-LSTM framework in small sample oil and gas pipeline leakage detectionHongyu Gao0Fenghua Hao1Yiwen Zhang2Xueyan Song3Nan Hou4National Key Laboratory of Continental Shale Oil, Northeast Petroleum University, Daqing 163318, China; Artificial Intelligence Energy Research Institute, Northeast Petroleum University, Daqing 163318, China; School of Electrical and Information Engineering, Northeast Petroleum University, Daqing 163318, China; Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control, Northeast Petroleum University, Daqing 163318, ChinaNational Key Laboratory of Continental Shale Oil, Northeast Petroleum University, Daqing 163318, China; Artificial Intelligence Energy Research Institute, Northeast Petroleum University, Daqing 163318, China; School of Electrical and Information Engineering, Northeast Petroleum University, Daqing 163318, China; Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control, Northeast Petroleum University, Daqing 163318, ChinaNational Key Laboratory of Continental Shale Oil, Northeast Petroleum University, Daqing 163318, China; Artificial Intelligence Energy Research Institute, Northeast Petroleum University, Daqing 163318, China; School of Electrical and Information Engineering, Northeast Petroleum University, Daqing 163318, China; Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control, Northeast Petroleum University, Daqing 163318, ChinaNational Key Laboratory of Continental Shale Oil, Northeast Petroleum University, Daqing 163318, China; Artificial Intelligence Energy Research Institute, Northeast Petroleum University, Daqing 163318, China; School of Electrical and Information Engineering, Northeast Petroleum University, Daqing 163318, China; Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control, Northeast Petroleum University, Daqing 163318, ChinaNational Key Laboratory of Continental Shale Oil, Northeast Petroleum University, Daqing 163318, China; Sanya Offshore Oil & Gas Research Institute, Northeast Petroleum University, Sanya 572025, China; Artificial Intelligence Energy Research Institute, Northeast Petroleum University, Daqing 163318, China; Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control, Northeast Petroleum University, Daqing 163318, China; Corresponding author at: National Key Laboratory of Continental Shale Oil, Northeast Petroleum University, Daqing 163318, China.In this article, a novel ensemble framework of improved siamese network (SN) is proposed to address the small sample issue that deep learning approaches encounter, as well as to enhance the precision of pipeline leakage detection (PLD) under small sample conditions. Firstly, training samples are input in pairs to the feature extraction network, and a combination of one-dimensional convolution neural network (1DCNN) and long short-term memory (LSTM) network is introduced to extract features of the time-series data, thus enhancing the effectiveness and robustness of feature extraction. Then, an improved relational metric network is designed to measure the similarity of features, to further strengthen the discriminative nature of the whole framework. In addition, the framework has been augmented with a classification network that can be used directly for PLD. The proposed SN-1DCNN-LSTM framework not only increases the number of training samples from the side, but also fully exploits the similarity information and data features between samples, overcoming the problem of instability and overfitting of deep learning models under small sample conditions. Finally, the experimental results verify the validity and superiority of the method under small sample conditions.http://www.sciencedirect.com/science/article/pii/S2773186324000045Pipeline leakage detectionSmall sampleSiamese networkLong short-term memoryOne-dimensional convolution neural networkTime-series |
| spellingShingle | Hongyu Gao Fenghua Hao Yiwen Zhang Xueyan Song Nan Hou Application of Novel SN-1DCNN-LSTM framework in small sample oil and gas pipeline leakage detection Franklin Open Pipeline leakage detection Small sample Siamese network Long short-term memory One-dimensional convolution neural network Time-series |
| title | Application of Novel SN-1DCNN-LSTM framework in small sample oil and gas pipeline leakage detection |
| title_full | Application of Novel SN-1DCNN-LSTM framework in small sample oil and gas pipeline leakage detection |
| title_fullStr | Application of Novel SN-1DCNN-LSTM framework in small sample oil and gas pipeline leakage detection |
| title_full_unstemmed | Application of Novel SN-1DCNN-LSTM framework in small sample oil and gas pipeline leakage detection |
| title_short | Application of Novel SN-1DCNN-LSTM framework in small sample oil and gas pipeline leakage detection |
| title_sort | application of novel sn 1dcnn lstm framework in small sample oil and gas pipeline leakage detection |
| topic | Pipeline leakage detection Small sample Siamese network Long short-term memory One-dimensional convolution neural network Time-series |
| url | http://www.sciencedirect.com/science/article/pii/S2773186324000045 |
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