Acoustic Emission-Based Small Leak Detection of Propulsion System Pipeline of Sounding Rocket
For pipes connected by pipe joints, leaks in the pipeline system are likely to occur at the pipe joints as opposed to the tube itself. Thus, early detection is critical to ensure the safety of the pipeline system. Based on acoustic emission (AE) techniques, this paper presents an experimental resear...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2020/8875939 |
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| _version_ | 1849682812240658432 |
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| author | Lin Gao Lili Dong Jianguo Cao Shaofeng Wang Wenjing Liu |
| author_facet | Lin Gao Lili Dong Jianguo Cao Shaofeng Wang Wenjing Liu |
| author_sort | Lin Gao |
| collection | DOAJ |
| description | For pipes connected by pipe joints, leaks in the pipeline system are likely to occur at the pipe joints as opposed to the tube itself. Thus, early detection is critical to ensure the safety of the pipeline system. Based on acoustic emission (AE) techniques, this paper presents an experimental research on small leak detection in gas distribution pipelines due to loosening of the pipe joint connection. Firstly, the acoustic characteristics of leak signals are studied; then, features of signals are extracted. Finally, a classifier based on the support vector machine (SVM) technology is established, and the qualified features are selected to detect the leak. It is verified that the main frequency of the AE small leak signal due to the failure of the pipe joint is focused in the range of 33–45 kHz, and the algorithms based on SVM with kernel functions all can reach a better estimation accuracy of 98% using the feature “envelope area” or the feature set {standard deviation (STD), root mean square (RMS), energy, average frequency}. |
| format | Article |
| id | doaj-art-ca7a6a9845a94aa89a457c085bb3f81f |
| institution | DOAJ |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-ca7a6a9845a94aa89a457c085bb3f81f2025-08-20T03:24:04ZengWileyShock and Vibration1070-96221875-92032020-01-01202010.1155/2020/88759398875939Acoustic Emission-Based Small Leak Detection of Propulsion System Pipeline of Sounding RocketLin Gao0Lili Dong1Jianguo Cao2Shaofeng Wang3Wenjing Liu4University of Science & Technology Beijing, Sch Mech Engn, 30 Xueyuan Rd, Beijing 100083, ChinaInner Mongolia Key Laboratory of Intelligent Diagnosis and Control of Mechatronic Systems, Inner Mongolia University of Science and Technology, No. 7 Arden Ave, Baotou, Inner Mongolia 014010, ChinaUniversity of Science & Technology Beijing, Sch Mech Engn, 30 Xueyuan Rd, Beijing 100083, ChinaInner Mongolia Key Laboratory of Intelligent Diagnosis and Control of Mechatronic Systems, Inner Mongolia University of Science and Technology, No. 7 Arden Ave, Baotou, Inner Mongolia 014010, ChinaInner Mongolia Key Laboratory of Intelligent Diagnosis and Control of Mechatronic Systems, Inner Mongolia University of Science and Technology, No. 7 Arden Ave, Baotou, Inner Mongolia 014010, ChinaFor pipes connected by pipe joints, leaks in the pipeline system are likely to occur at the pipe joints as opposed to the tube itself. Thus, early detection is critical to ensure the safety of the pipeline system. Based on acoustic emission (AE) techniques, this paper presents an experimental research on small leak detection in gas distribution pipelines due to loosening of the pipe joint connection. Firstly, the acoustic characteristics of leak signals are studied; then, features of signals are extracted. Finally, a classifier based on the support vector machine (SVM) technology is established, and the qualified features are selected to detect the leak. It is verified that the main frequency of the AE small leak signal due to the failure of the pipe joint is focused in the range of 33–45 kHz, and the algorithms based on SVM with kernel functions all can reach a better estimation accuracy of 98% using the feature “envelope area” or the feature set {standard deviation (STD), root mean square (RMS), energy, average frequency}.http://dx.doi.org/10.1155/2020/8875939 |
| spellingShingle | Lin Gao Lili Dong Jianguo Cao Shaofeng Wang Wenjing Liu Acoustic Emission-Based Small Leak Detection of Propulsion System Pipeline of Sounding Rocket Shock and Vibration |
| title | Acoustic Emission-Based Small Leak Detection of Propulsion System Pipeline of Sounding Rocket |
| title_full | Acoustic Emission-Based Small Leak Detection of Propulsion System Pipeline of Sounding Rocket |
| title_fullStr | Acoustic Emission-Based Small Leak Detection of Propulsion System Pipeline of Sounding Rocket |
| title_full_unstemmed | Acoustic Emission-Based Small Leak Detection of Propulsion System Pipeline of Sounding Rocket |
| title_short | Acoustic Emission-Based Small Leak Detection of Propulsion System Pipeline of Sounding Rocket |
| title_sort | acoustic emission based small leak detection of propulsion system pipeline of sounding rocket |
| url | http://dx.doi.org/10.1155/2020/8875939 |
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