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A High Sensitivity AlN-Based MEMS Hydrophone for Pipeline Leak Monitoring.
Zhi, Baoyu; Wu, Zhipeng; Chen, Caihui; Chen, Minkan; Ding, Xiaoxia; Lou, Liang.
Afiliación
  • Zhi B; School of Microelectronics, Shanghai University, Shanghai 201800, China.
  • Wu Z; The Shanghai Industrial µ Technology Research Institute, Shanghai 201899, China.
  • Chen C; The Shanghai Industrial µ Technology Research Institute, Shanghai 201899, China.
  • Chen M; The Shanghai Industrial µ Technology Research Institute, Shanghai 201899, China.
  • Ding X; The Shanghai Industrial µ Technology Research Institute, Shanghai 201899, China.
  • Lou L; School of Microelectronics, Shanghai University, Shanghai 201800, China.
Micromachines (Basel) ; 14(3)2023 Mar 14.
Article en En | MEDLINE | ID: mdl-36985061
In this work, a miniaturized, low-cost, low-power and high-sensitivity AlN-based micro-electro-mechanical system (MEMS) hydrophone is proposed for monitoring water pipeline leaks. The proposed MEMS Hydrophone consists of a piezoelectric micromachined ultrasonic transducer (PMUT) array, an acoustic matching layer and a pre-amplifier amplifier circuit. The array has 4 (2 × 2) PMUT elements with a first-order resonant frequency of 41.58 kHz. Due to impedance matching of the acoustic matching layer and the 40 dB gain of the pre-amplifier amplifier circuit, the packaged MEMS Hydrophone has a high sound pressure sensitivity of -170 ± 2 dB (re: 1 V/µPa). The performance with respect to detecting pipeline leaks and locating leak points is demonstrated on a 31 m stainless leaking pipeline platform. The standard deviation (STD) of the hydroacoustic signal and Monitoring Index Efficiency (MIE) are extracted as features of the pipeline leak. A random forest model is trained for accurately classifying the leak and no-leak cases using the above features, and the accuracy of the model is about 97.69%. The cross-correlation method is used to locate the leak point, and the localization relative error is about 10.84% for a small leak of 12 L/min.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Diagnostic_studies Idioma: En Revista: Micromachines (Basel) Año: 2023 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Diagnostic_studies Idioma: En Revista: Micromachines (Basel) Año: 2023 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza