Design and Test of Pipeline Leak Detector With Acoustic Resonance Air Cavity
Early detection of continuous small leaks in pipeline infrastructure is crucial for preventing major failures, yet existing methods often struggle with sensitivity limitations and practical deployment challenges. This article presents an innovative approach using a spherical detector (SD) with an acoustic resonant air cavity (ARAC), which addresses these challenges by combining the advantages of free-swimming mobility with enhanced acoustic sensitivity. The ARAC design leverages acoustic resonance principles to amplify leak-induced sounds, while the SD’s mobility allows direct access to leak locations within pipelines. Through systematic investigation of cavity dimensions and resonance modes via finite element analysis, we establish the relationship between ARAC size and detection sensitivity. Laboratory experiments with multiple SD prototypes demonstrate that larger cavities (>96 mm diameter) achieve superior sensitivity by effectively capturing low-frequency (100–5000 Hz) leak signals through their first three resonance modes. A signal processing algorithm combining wavelet decomposition and adaptive thresholding is developed to distinguish leak signals from mechanical impacts, achieving detection rates exceeding 95% for leaks 0.6–1.0 mm in diameter. Field validation tests with an optimized aluminum SD prototype confirm the reliable detection of 1 mm leaks under both high (1 MPa) and low (0.5 MPa) pressure conditions while rolling through operational pipelines. Quantitative analysis reveals that the system maintains effective detection capability even under challenging field conditions, with the algorithm successfully filtering out interference from pipe wall collisions and environmental noise. These findings demonstrate the practical viability of ARAC-based detection technology for pipeline infrastructure monitoring and maintenance.
Authors
- Xiaoyu Ren (ORCID: https://orcid.org/0000-0001-8724-3635)
- Xinjing Huang (ORCID: https://orcid.org/0000-0002-8964-8502)
- Bian Xu (ORCID: https://orcid.org/0000-0001-5503-5567)
- Liang Wang (ORCID: https://orcid.org/0000-0003-2422-0916)
- Jinyu Ma (ORCID: https://orcid.org/0000-0003-1509-9421)
- Jian Li (ORCID: https://orcid.org/0000-0002-0149-8299)
Institutions
- Tianjin University (CN)
Publication Details
- Journal
- IEEE Sensors Journal
- Published
- 2024-12-25
- DOI
- https://doi.org/10.1109/jsen.2024.3520131
- Citations
- 1
- Primary Topic
- IoT-based Smart Home Systems
- Type
- article
- Field-Weighted Citation Impact
- 0.21
Funders
- National Natural Science Foundation of China
- Guangxi Key Laboratory of Automatic Detection Technology and Instrument Foundation