A Deep CNN-Based Ground Vibration Monitoring Scheme for MEMS Sensed Data

Kang, Jae-Mo; Kim, Il-Min; Lee, Sangho; Ryu, Dong-Woo; Kwon, Jihoe

doi:10.1109/lgrs.2019.2918641

Cited by 11 publications

(5 citation statements)

References 14 publications

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“…Down-sampling or sparse-sampling has been investigated in several studies, with consistent and interesting results. For instance, Cohen et al (2018) [39] demonstrated the advantages of spatial and temporal down-sampling in event-based visual classification, while Kang et al (2020) [40] examined the effects of uniform down-sampling in a deep CNN-based ground vibration monitoring scheme for MEMS sensed data. Similarly, Naagome et al (2020) [41] showed that down-sampling increased the accuracy of RNNs in decoding gait from EEG data.…”

Section: Figure 14mentioning

confidence: 99%

Quasi-Distributed Fiber Sensor-Based Approach for Pipeline Health Monitoring: Generating and Analyzing Physics-Based Simulation Datasets for Classification

Zhang

Venketeswaran

Wright

et al. 2023

Sensors

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This study presents a framework for detecting mechanical damage in pipelines, focusing on generating simulated data and sampling to emulate distributed acoustic sensing (DAS) system responses. The workflow transforms simulated ultrasonic guided wave (UGW) responses into DAS or quasi-DAS system responses to create a physically robust dataset for pipeline event classification, including welds, clips, and corrosion defects. This investigation examines the effects of sensing systems and noise on classification performance, emphasizing the importance of selecting the appropriate sensing system for a specific application. The framework shows the robustness of different sensor number deployments to experimentally relevant noise levels, demonstrating its applicability in real-world scenarios where noise is present. Overall, this study contributes to the development of a more reliable and effective method for detecting mechanical damage to pipelines by emphasizing the generation and utilization of simulated DAS system responses for pipeline classification efforts. The results on the effects of sensing systems and noise on classification performance further enhance the robustness and reliability of the framework.

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Section: Figure 14mentioning

confidence: 99%

Quasi-Distributed Fiber Sensor-Based Approach for Pipeline Health Monitoring: Generating and Analyzing Physics-Based Simulation Datasets for Classification

Zhang

Venketeswaran

Wright

et al. 2023

Sensors

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show abstract

“…Down-sampling or sparse-sampling has been investigated in several studies, with consistent and interesting results. For instance, Cohen et al (2018) [37] demonstrated the advantages of spatial and temporal down-sampling in event-based visual classification, while Kang et al (2020) [38] examined the effects of uniform down-sampling in a deep CNN-based ground vibration monitoring scheme for MEMS sensed data. Similarly, Naagome et al (2020) [39] showed that down-sampling increased the accuracy of RNNs in decoding gait from EEG data.…”

Section: Figure 15mentioning

confidence: 99%

Real-Time Pipe Structure Change Detection and Classification using Distributed Acoustic Fiber Sensors Based on Convolutional Neural Network (CNN) Models

Zhang¹,

Venketeswaran²,

Wright³

et al. 2023

Preprint

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This study proposes a machine-learning-based framework for detecting mechanical damage in pipelines, utilizing physics-informed datasets collected from simulations for mechanical damage. The framework provides an effective workflow from dataset generation to damage detection and identification for three types of pipeline events: welds, clamps, and corrosion defects. While the study initially focused on optimizing the CNN structure using various advanced optimizers, it also investigated the impact of sensing systems on data classification and the effect of noise on classification performance. The study's analysis highlights the importance of selecting the appropriate sensing system for the specific application. The authors also found that the proposed framework is robust to experimentally relevant levels of noise, suggesting its applicability in real-world scenarios where noise is present. Overall, this study contributes to the development of a more reliable and effective method for detecting mechanical damage in pipelines. The proposed framework provides an effective workflow for damage detection and identification, and the findings on the impact of sensing systems and noise on classification performance add to its robustness and reliability.

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“…MEMS sensors such as the ADXL 335 accelerometer (±3 g, 270-330 mV g −1 , 150-300 µg Hz −1 rms, 0.5-1600 Hz) are able to provide a richer source of blast-induced damage monitoring through their ability to measure acceleration, velocity and displacement [134,136]. However, measurement errors associated with noise, offset and phase shifts are required to be resolved and currently being tackled by a deep convolutional neural network [137]. Once resolved, MEMS sensors will become more prevalent for blast-induced ground vibration monitoring to overcome the existing systems shortcomings associated with cost, insufficient real time monitoring, limited storage memory and time consuming post-processing [134].…”

Section: Blast-induced Ground Vibrationmentioning

confidence: 99%

MEMS technology and applications in geotechnical monitoring: a review

Barzegar

Blanks²,

Sainsbury

et al. 2022

Meas. Sci. Technol.

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In-situ monitoring is an important aspect of geotechnical projects to ensure safety and optimise design measures. However, existing conventional monitoring instruments are limited in their accuracy, durability, complex and high cost of installation and requirement for ongoing real time measurement. Advancements in sensing technology in recent years have created a unique prospect for geotechnical monitoring to overcome some of those limitations. For this reason, micro-electro-mechanical system (MEMS) technology has gained popularity for geotechnical monitoring. MEMS devices combine both mechanical and electrical components to convert environment system stimuli to electrical signals. MEMS-based sensors provide advantages to traditional sensors in that they are millimetre to micron sized and sufficiently inexpensive to be ubiquitously distributed within an environment or structure. This ensures that the monitoring of the in-situ system goes beyond discrete point data but provides an accurate assessment of the entire structures response. The capability to operate with wireless technology makes MEMS microsensors even more desirable in geotechnical monitoring where dynamic changes in heterogeneous materials at great depth and over large areas are expected. Many of these locations are remote or hazardous to access directly and are thus a target for MEMS development. This paper provides a review of current applications of existing MEMS technology to the field/s of geotechnical engineering and provides a path forward for the expansion of this research and commercialisation of products.

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A Deep CNN-Based Ground Vibration Monitoring Scheme for MEMS Sensed Data

Cited by 11 publications

References 14 publications

Quasi-Distributed Fiber Sensor-Based Approach for Pipeline Health Monitoring: Generating and Analyzing Physics-Based Simulation Datasets for Classification

Quasi-Distributed Fiber Sensor-Based Approach for Pipeline Health Monitoring: Generating and Analyzing Physics-Based Simulation Datasets for Classification

Real-Time Pipe Structure Change Detection and Classification using Distributed Acoustic Fiber Sensors Based on Convolutional Neural Network (CNN) Models

MEMS technology and applications in geotechnical monitoring: a review

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