A High Sensitivity AlN-Based MEMS Hydrophone for Pipeline Leak Monitoring

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 m...

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Bibliographic Details
Published in:Micromachines (Basel) 2023-03, Vol.14 (3), p.654
Main Authors: Zhi, Baoyu, Wu, Zhipeng, Chen, Caihui, Chen, Minkan, Ding, Xiaoxia, Lou, Liang
Format: Article
Language:English
Subjects:
Accelerometers
Accuracy
Acoustic impedance
Acoustics
Aluminum compounds
Amplification
Amplifiers
Amplifiers (Electronics)
Arrays
Circuits
Communication
Cross correlation
Design and construction
Electrodes
Etching
Freshwater resources
Hydrophones
Impedance matching
Internet of Things
Leak detection
leak localization
Localization
Matching layers (electronics)
MEMS hydrophone
Microelectromechanical systems
Micromachining
Model accuracy
Piezoelectricity
Pipe lines
Pipelines
PMUT
Resonant frequencies
Sensitivity
Sensors
Signal monitoring
Sound pressure
Ultrasonic transducers
Water pipelines
Zinc oxides
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Summary: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.
ISSN:2072-666X
2072-666X
DOI:10.3390/mi14030654