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

Zhi, Baoyu; Wu, Zhipeng; Chen, Caihui; Chen, Minkan; Ding, Xiaoxia; Lou, Liang

doi:10.3390/mi14030654

Cited by 4 publications

(2 citation statements)

References 40 publications

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“…Zhi Baoyu [59] and others developed an AlN-based MEMS hydrophone with miniaturization, low cost, low power consumption, and high sensitivity to monitor the leakage of water pipelines. Zaqretdinov Ayrat [60] and others analyzed the acoustic signals recorded from the pipeline surface under different leaks and water pressures and established a relationship between the size of the leakage and the Hurst index of the acoustic signals.…”

Section: Sound Sensormentioning

confidence: 99%

Review of Sensor-Based Subgrade Distress Identifications

Cheng,

Xie,

Wei

et al. 2024

Sensors

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The attributes of diversity and concealment pose formidable challenges in the accurate detection and efficacious management of distresses within subgrade structures. The onset of subgrade distresses may precipitate structural degradation, thereby amplifying the frequency of traffic incidents and instigating economic ramifications. Accurate and timely detection of subgrade distresses is essential for maintaining and repairing road sections with existing distresses. This helps to prolong the service life of road infrastructure and reduce financial burden. In recent years, the advent of numerous novel technologies and methodologies has propelled significant advancements in subgrade distress detection. Therefore, this review delineates a concentrated examination of subgrade distress detection, methodically consolidating and presenting various techniques while dissecting their respective merits and constraints. By furnishing comprehensive guidance on subgrade distress detection, this review facilitates the expedient identification and targeted treatment of subgrade distresses, thereby fortifying safety and enhancing durability. The pivotal role of this review in bolstering the construction and operational facets of transportation infrastructure is underscored.

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Section: Sound Sensormentioning

confidence: 99%

Review of Sensor-Based Subgrade Distress Identifications

Cheng,

Xie,

Wei

et al. 2024

Sensors

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“…After the completion of the design and production of the hydrophone should be the experimental verification of the hydrophone, first of all, the use of COMSOL simulation software for simulation verification. Eigenfrequency is an important indicator of the performance of the MEMS hydrophone, the upper limit of the actual measurement frequency of the hydrophone can not exceed the lowest order eigenfrequency of the MEMS hydrophone, and only in this way can the frequency response of the structure consistent and work stably [19].…”

Section: Hydrophone Simulation Verificationmentioning

confidence: 99%

A MEMS piezoelectric hydrophone with high sensitivity and wide bandwidth

Liu,

Zhang,

Zhang

et al. 2024

Phys. Scr.

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To solve the problems of low sound pressure sensitivity and narrow operating bandwidth of hydrophones, this paper proposes a MEMS piezoelectric hydrophone with a corrugated structure substrate. This design is improved from two aspects, on the one hand, the MEMS technology is used to make the aluminum oxide substrate into a corrugated structure, the corrugated structure can make the hydrophone work with better linearity, and at the same time broaden the hydrophone's operating bandwidth; on the other hand, the array design is utilized to improve the sound pressure sensitivity by integrating four identical sensitive cores in the same piece of hydrophone. The test results show that the sound pressure sensitivity of the MEMS piezoelectric hydrophone of this design is better than -207.6 dB in the operating bandwidth of 10~3800 Hz, and reaches the highest -195.5 dB sound pressure sensitivity at the input signal frequency of 2500 Hz.

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Data-Driven Approaches for Vibroacoustic Localization of Leaks in Water Distribution Networks

Liu,

Tariq,

Tijani

et al. 2024

Environ. Process.

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This study aims to propose Micro-electromechanical System (MEMS) accelerometers for leak localization in the water distribution network and assess the performance of machine learning models in accurately estimating leak locations. Intensive field experimentation was conducted to collect data for model development. Machine learning algorithms were employed to develop leak localization models, specifically artificial neural network (ANN) and support vector machine (SVM). Seventeen time-domain and frequency-domain features were extracted, and feature selection was performed using the backward elimination method. The results indicate that the ANN and SVM models are suitable classifiers for localizing leak distance. Both models achieved leak location predictions with over 80% accuracy, and the mean absolute errors were measured at 0.858 and 0.95 for the ANN and SVM models, respectively. The validation results demonstrated that the models maintained accuracies close to 80% when the distance between sensors and the leak was less than 15 m. However, the performance of the model deteriorates when leaks occur at distances greater than 15 m. This study demonstrates the applicability of MEMS accelerometers for leak localization in water distribution networks. The findings highlight the promising potential of employing MEMS accelerometers-based ANN and SVM models for accurate leak localization in urban networks, even under real-world, uncontrolled conditions. However, the current model exhibits limited performance in long-distance leak localization, requiring further research to address and resolve this issue.

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

Cited by 4 publications

References 40 publications

Review of Sensor-Based Subgrade Distress Identifications

Review of Sensor-Based Subgrade Distress Identifications

A MEMS piezoelectric hydrophone with high sensitivity and wide bandwidth

Data-Driven Approaches for Vibroacoustic Localization of Leaks in Water Distribution Networks

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