Real-Time Leak Detection for a Gas Pipeline Using a k-NN Classifier and Hybrid AE Features
- Abstract
- This paper introduces a technique using a k-nearest neighbor (k-NN) classifier and hybrid features extracted from acoustic emission (AE) signals for detecting leakages in a gas pipeline. The whole algorithm is embedded in a microcontroller unit (MCU) to detect leaks in real-time. The embedded system receives signals continuously from a sensor mounted on the surface of a gas pipeline to diagnose any leak. To construct the system, AE signals are first recorded from a gas pipeline testbed under various conditions and used to synthesize the leak detection algorithm via offline signal analysis. The current work explores different features of normal/leaking states from corresponding datasets and eliminates redundant and outlier features to improve the performance and guarantee the real-time characteristic of the leak detection program. To obtain the robustness of leak detection, the paper normalizes features and adapts the trained k-NN classifier to the specific environment where the system is installed. Aside from using a classifier for categorizing normal/leaking states of a pipeline, the system monitors accumulative leaking event occurrence rate (ALEOR) in conjunction with a defined threshold to conclude the state of the pipeline. The entire proposed system is implemented on the 32F746G-DISCOVERY board, and to verify this system, numerous real AE signals stored in a hard drive are transferred to the board. The experimental results show that the proposed system executes the leak detection algorithm in a period shorter than the total input data time, thus guaranteeing the real-time characteristic. Furthermore, the system always yields high average classification accuracy (ACA) despite adding a white noise to input signal, and false alarms do not occur with a reasonable ALEOR threshold.
- Author(s)
- 부이 꾸이 탕; 김종면
- Issued Date
- 2021
- Type
- Article
- Keyword
- acoustic emission analysis; hybrid AE features; k-NN algorithm; pipeline leak detection; signal classification
- DOI
- 10.3390/s21020367
- URI
- https://oak.ulsan.ac.kr/handle/2021.oak/9039
https://ulsan-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_cdi_doaj_primary_oai_doaj_org_article_de974d7e0aaf4bb28b5babf8ddf3eeda&context=PC&vid=ULSAN&lang=ko_KR&search_scope=default_scope&adaptor=primo_central_multiple_fe&tab=default_tab&query=any,contains,Real-Time%20Leak%20Detection%20for%20a%20Gas%20Pipeline%20Using%20a%20k-NN%20Classifier%20and%20Hybrid%20AE%20Features&offset=0&pcAvailability=true
- Publisher
- SENSORS
- Location
- 스위스
- Language
- 영어
- ISSN
- 1424-8220
- Citation Volume
- 21
- Citation Number
- 2
- Citation Start Page
- 367
- Citation End Page
- 367
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- Engineering > IT Convergence
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