Research on Pattern Recognition Method for φ-OTDR System Based on Dendrite Net
The phase-sensitive optical time-domain reflectometer (φ-OTDR) is commonly used in various industries such as oil and gas pipelines, power communication networks, safety maintenance, and perimeter security. However, one challenge faced by the φ-OTDR system is low pattern recognition accuracy. To ove...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/12/18/3757 |
| _version_ | 1797580443779858432 |
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| author | Xiaojuan Chen Cheng Yang Haoyu Yu Guangwei Hou |
| author_facet | Xiaojuan Chen Cheng Yang Haoyu Yu Guangwei Hou |
| author_sort | Xiaojuan Chen |
| collection | DOAJ |
| description | The phase-sensitive optical time-domain reflectometer (φ-OTDR) is commonly used in various industries such as oil and gas pipelines, power communication networks, safety maintenance, and perimeter security. However, one challenge faced by the φ-OTDR system is low pattern recognition accuracy. To overcome this issue, a Dendrite Net (DD)-based pattern recognition method is proposed to differentiate the vibration signals detected by the φ-OTDR system, and normalize the differential signals with the original signals for feature extraction. These features serve as input for the pattern recognition task. To optimize the DD for the pattern recognition of the feature vectors, the Variable Three-Term Conjugate Gradient (VTTCG) is employed. The experimental results demonstrate the effectiveness of the proposed method. The classification accuracy achieved using this method is 98.6%, which represents a significant improvement compared to other techniques. Specifically, the proposed method outperforms the DD, Support Vector Machine (SVM), and Extreme Learning Machine (ELM) by 7.5%, 8.6%, and 1.5% respectively. The findings of this research paper indicate that the pattern recognition method based on DD and optimized using the VTTCG can greatly enhance the accuracy of the φ-OTDR system. This improvement has important implications for various applications in industries such as pipeline monitoring, power communication networks, safety maintenance, and perimeter security. |
| first_indexed | 2024-03-10T22:51:00Z |
| format | Article |
| id | doaj.art-35be51ebdc964e25b7efe2e911f59f6b |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T22:51:00Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-35be51ebdc964e25b7efe2e911f59f6b2023-11-19T10:20:57ZengMDPI AGElectronics2079-92922023-09-011218375710.3390/electronics12183757Research on Pattern Recognition Method for φ-OTDR System Based on Dendrite NetXiaojuan Chen0Cheng Yang1Haoyu Yu2Guangwei Hou3College of Electronical and Information Engineering, Changchun University of Science and Technology, Changchun 130022, ChinaCollege of Electronical and Information Engineering, Changchun University of Science and Technology, Changchun 130022, ChinaCollege of Electronical and Information Engineering, Changchun University of Science and Technology, Changchun 130022, ChinaElectric Power Dispatching Control Center, Tonghua Power Supply Company of State Grid Jilin Electric Power Co., Ltd., Tonghua 134000, ChinaThe phase-sensitive optical time-domain reflectometer (φ-OTDR) is commonly used in various industries such as oil and gas pipelines, power communication networks, safety maintenance, and perimeter security. However, one challenge faced by the φ-OTDR system is low pattern recognition accuracy. To overcome this issue, a Dendrite Net (DD)-based pattern recognition method is proposed to differentiate the vibration signals detected by the φ-OTDR system, and normalize the differential signals with the original signals for feature extraction. These features serve as input for the pattern recognition task. To optimize the DD for the pattern recognition of the feature vectors, the Variable Three-Term Conjugate Gradient (VTTCG) is employed. The experimental results demonstrate the effectiveness of the proposed method. The classification accuracy achieved using this method is 98.6%, which represents a significant improvement compared to other techniques. Specifically, the proposed method outperforms the DD, Support Vector Machine (SVM), and Extreme Learning Machine (ELM) by 7.5%, 8.6%, and 1.5% respectively. The findings of this research paper indicate that the pattern recognition method based on DD and optimized using the VTTCG can greatly enhance the accuracy of the φ-OTDR system. This improvement has important implications for various applications in industries such as pipeline monitoring, power communication networks, safety maintenance, and perimeter security.https://www.mdpi.com/2079-9292/12/18/3757φ-OTDRDendrite Netpattern recognitionvariable three-term conjugate gradient method |
| spellingShingle | Xiaojuan Chen Cheng Yang Haoyu Yu Guangwei Hou Research on Pattern Recognition Method for φ-OTDR System Based on Dendrite Net Electronics φ-OTDR Dendrite Net pattern recognition variable three-term conjugate gradient method |
| title | Research on Pattern Recognition Method for φ-OTDR System Based on Dendrite Net |
| title_full | Research on Pattern Recognition Method for φ-OTDR System Based on Dendrite Net |
| title_fullStr | Research on Pattern Recognition Method for φ-OTDR System Based on Dendrite Net |
| title_full_unstemmed | Research on Pattern Recognition Method for φ-OTDR System Based on Dendrite Net |
| title_short | Research on Pattern Recognition Method for φ-OTDR System Based on Dendrite Net |
| title_sort | research on pattern recognition method for φ otdr system based on dendrite net |
| topic | φ-OTDR Dendrite Net pattern recognition variable three-term conjugate gradient method |
| url | https://www.mdpi.com/2079-9292/12/18/3757 |
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