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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Autor*in:	Baoyu Zhi [verfasserIn] Zhipeng Wu [verfasserIn] Caihui Chen [verfasserIn] Minkan Chen [verfasserIn] Xiaoxia Ding [verfasserIn] Liang Lou [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	MEMS hydrophone PMUT leak detection leak localization

Übergeordnetes Werk:	In: Micromachines - MDPI AG, 2010, 14(2023), 3, p 654
Übergeordnetes Werk:	volume:14 ; year:2023 ; number:3, p 654

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/mi14030654

Katalog-ID:	DOAJ087290626

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520			\|a 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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allfields	10.3390/mi14030654 doi (DE-627)DOAJ087290626 (DE-599)DOAJ023bb39b5bf94939a8d0c710f32ba68e DE-627 ger DE-627 rakwb eng TJ1-1570 Baoyu Zhi verfasserin aut A High Sensitivity AlN-Based MEMS Hydrophone for Pipeline Leak Monitoring 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. MEMS hydrophone PMUT leak detection leak localization Mechanical engineering and machinery Zhipeng Wu verfasserin aut Caihui Chen verfasserin aut Minkan Chen verfasserin aut Xiaoxia Ding verfasserin aut Liang Lou verfasserin aut In Micromachines MDPI AG, 2010 14(2023), 3, p 654 (DE-627)665016069 (DE-600)2620864-7 2072666X nnns volume:14 year:2023 number:3, p 654 https://doi.org/10.3390/mi14030654 kostenfrei https://doaj.org/article/023bb39b5bf94939a8d0c710f32ba68e kostenfrei https://www.mdpi.com/2072-666X/14/3/654 kostenfrei https://doaj.org/toc/2072-666X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2023 3, p 654
spelling	10.3390/mi14030654 doi (DE-627)DOAJ087290626 (DE-599)DOAJ023bb39b5bf94939a8d0c710f32ba68e DE-627 ger DE-627 rakwb eng TJ1-1570 Baoyu Zhi verfasserin aut A High Sensitivity AlN-Based MEMS Hydrophone for Pipeline Leak Monitoring 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. MEMS hydrophone PMUT leak detection leak localization Mechanical engineering and machinery Zhipeng Wu verfasserin aut Caihui Chen verfasserin aut Minkan Chen verfasserin aut Xiaoxia Ding verfasserin aut Liang Lou verfasserin aut In Micromachines MDPI AG, 2010 14(2023), 3, p 654 (DE-627)665016069 (DE-600)2620864-7 2072666X nnns volume:14 year:2023 number:3, p 654 https://doi.org/10.3390/mi14030654 kostenfrei https://doaj.org/article/023bb39b5bf94939a8d0c710f32ba68e kostenfrei https://www.mdpi.com/2072-666X/14/3/654 kostenfrei https://doaj.org/toc/2072-666X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2023 3, p 654
allfields_unstemmed	10.3390/mi14030654 doi (DE-627)DOAJ087290626 (DE-599)DOAJ023bb39b5bf94939a8d0c710f32ba68e DE-627 ger DE-627 rakwb eng TJ1-1570 Baoyu Zhi verfasserin aut A High Sensitivity AlN-Based MEMS Hydrophone for Pipeline Leak Monitoring 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. MEMS hydrophone PMUT leak detection leak localization Mechanical engineering and machinery Zhipeng Wu verfasserin aut Caihui Chen verfasserin aut Minkan Chen verfasserin aut Xiaoxia Ding verfasserin aut Liang Lou verfasserin aut In Micromachines MDPI AG, 2010 14(2023), 3, p 654 (DE-627)665016069 (DE-600)2620864-7 2072666X nnns volume:14 year:2023 number:3, p 654 https://doi.org/10.3390/mi14030654 kostenfrei https://doaj.org/article/023bb39b5bf94939a8d0c710f32ba68e kostenfrei https://www.mdpi.com/2072-666X/14/3/654 kostenfrei https://doaj.org/toc/2072-666X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2023 3, p 654
allfieldsGer	10.3390/mi14030654 doi (DE-627)DOAJ087290626 (DE-599)DOAJ023bb39b5bf94939a8d0c710f32ba68e DE-627 ger DE-627 rakwb eng TJ1-1570 Baoyu Zhi verfasserin aut A High Sensitivity AlN-Based MEMS Hydrophone for Pipeline Leak Monitoring 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. MEMS hydrophone PMUT leak detection leak localization Mechanical engineering and machinery Zhipeng Wu verfasserin aut Caihui Chen verfasserin aut Minkan Chen verfasserin aut Xiaoxia Ding verfasserin aut Liang Lou verfasserin aut In Micromachines MDPI AG, 2010 14(2023), 3, p 654 (DE-627)665016069 (DE-600)2620864-7 2072666X nnns volume:14 year:2023 number:3, p 654 https://doi.org/10.3390/mi14030654 kostenfrei https://doaj.org/article/023bb39b5bf94939a8d0c710f32ba68e kostenfrei https://www.mdpi.com/2072-666X/14/3/654 kostenfrei https://doaj.org/toc/2072-666X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2023 3, p 654
allfieldsSound	10.3390/mi14030654 doi (DE-627)DOAJ087290626 (DE-599)DOAJ023bb39b5bf94939a8d0c710f32ba68e DE-627 ger DE-627 rakwb eng TJ1-1570 Baoyu Zhi verfasserin aut A High Sensitivity AlN-Based MEMS Hydrophone for Pipeline Leak Monitoring 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. MEMS hydrophone PMUT leak detection leak localization Mechanical engineering and machinery Zhipeng Wu verfasserin aut Caihui Chen verfasserin aut Minkan Chen verfasserin aut Xiaoxia Ding verfasserin aut Liang Lou verfasserin aut In Micromachines MDPI AG, 2010 14(2023), 3, p 654 (DE-627)665016069 (DE-600)2620864-7 2072666X nnns volume:14 year:2023 number:3, p 654 https://doi.org/10.3390/mi14030654 kostenfrei https://doaj.org/article/023bb39b5bf94939a8d0c710f32ba68e kostenfrei https://www.mdpi.com/2072-666X/14/3/654 kostenfrei https://doaj.org/toc/2072-666X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2023 3, p 654
language	English
source	In Micromachines 14(2023), 3, p 654 volume:14 year:2023 number:3, p 654
sourceStr	In Micromachines 14(2023), 3, p 654 volume:14 year:2023 number:3, p 654
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	MEMS hydrophone PMUT leak detection leak localization Mechanical engineering and machinery
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container_title	Micromachines
authorswithroles_txt_mv	Baoyu Zhi @@aut@@ Zhipeng Wu @@aut@@ Caihui Chen @@aut@@ Minkan Chen @@aut@@ Xiaoxia Ding @@aut@@ Liang Lou @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
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callnumber-first	T - Technology
author	Baoyu Zhi
spellingShingle	Baoyu Zhi misc TJ1-1570 misc MEMS hydrophone misc PMUT misc leak detection misc leak localization misc Mechanical engineering and machinery A High Sensitivity AlN-Based MEMS Hydrophone for Pipeline Leak Monitoring
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topic_title	TJ1-1570 A High Sensitivity AlN-Based MEMS Hydrophone for Pipeline Leak Monitoring MEMS hydrophone PMUT leak detection leak localization
topic	misc TJ1-1570 misc MEMS hydrophone misc PMUT misc leak detection misc leak localization misc Mechanical engineering and machinery
topic_unstemmed	misc TJ1-1570 misc MEMS hydrophone misc PMUT misc leak detection misc leak localization misc Mechanical engineering and machinery
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title	A High Sensitivity AlN-Based MEMS Hydrophone for Pipeline Leak Monitoring
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title_full	A High Sensitivity AlN-Based MEMS Hydrophone for Pipeline Leak Monitoring
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author_browse	Baoyu Zhi Zhipeng Wu Caihui Chen Minkan Chen Xiaoxia Ding Liang Lou
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title_sort	high sensitivity aln-based mems hydrophone for pipeline leak monitoring
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title_auth	A High Sensitivity AlN-Based MEMS Hydrophone for Pipeline Leak Monitoring
abstract	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.
abstractGer	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.
abstract_unstemmed	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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container_issue	3, p 654
title_short	A High Sensitivity AlN-Based MEMS Hydrophone for Pipeline Leak Monitoring
url	https://doi.org/10.3390/mi14030654 https://doaj.org/article/023bb39b5bf94939a8d0c710f32ba68e https://www.mdpi.com/2072-666X/14/3/654 https://doaj.org/toc/2072-666X
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