Smart sensing with edge intelligence for autonomous structural health monitoring

In the pursuit of Singapore becoming a Smart Nation, the integration of advanced technologies, such as sensors in structural health monitoring systems, has become paramount. This paper delves into the significance of sensor reliability in these monitoring systems. Sensor malfunctions, largely attrib...

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Main Author: Chen, John Yong Han
Other Authors: Fu Yuguang
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2023
Subjects:
Online Access:https://hdl.handle.net/10356/172429
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1724292023-12-15T15:35:00Z Smart sensing with edge intelligence for autonomous structural health monitoring Chen, John Yong Han Fu Yuguang School of Civil and Environmental Engineering yuguang.fu@ntu.edu.sg Engineering::Civil engineering In the pursuit of Singapore becoming a Smart Nation, the integration of advanced technologies, such as sensors in structural health monitoring systems, has become paramount. This paper delves into the significance of sensor reliability in these monitoring systems. Sensor malfunctions, largely attributed to external environmental factors and internal hardware issues, compromise the reliability of these systems. With this in mind, this study aims to develop an Artificial Intelligence (AI) framework that integrates edge computing, distributed learning, and structural health monitoring with sensor fault diagnosis. Given the increasing emphasis on sustainability, this paper also explores various compact AI models suitable for edge devices, their deep compression techniques, and the deployment of these models using federated machine learning. These models are evaluated based on Green AI criteria, such as training time, floating-point operations, and model size to identify the most optimally performing model. The study also discusses data pre-processing techniques, model compression methods, and deployment strategies on edge devices like the Raspberry Pi. The results from this study highlight the potential of using AI in structural health monitoring systems, offering potential solutions that are not only effective but also environmentally sustainable. Through the development and deployment of these compact models, the study sets the stage for the future of reliable and sustainable infrastructural health monitoring. Bachelor of Engineering (Civil) 2023-12-11T05:16:56Z 2023-12-11T05:16:56Z 2023 Final Year Project (FYP) Chen, J. Y. H. (2023). Smart sensing with edge intelligence for autonomous structural health monitoring. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/172429 https://hdl.handle.net/10356/172429 en ST-35AB application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Civil engineering
spellingShingle Engineering::Civil engineering
Chen, John Yong Han
Smart sensing with edge intelligence for autonomous structural health monitoring
description In the pursuit of Singapore becoming a Smart Nation, the integration of advanced technologies, such as sensors in structural health monitoring systems, has become paramount. This paper delves into the significance of sensor reliability in these monitoring systems. Sensor malfunctions, largely attributed to external environmental factors and internal hardware issues, compromise the reliability of these systems. With this in mind, this study aims to develop an Artificial Intelligence (AI) framework that integrates edge computing, distributed learning, and structural health monitoring with sensor fault diagnosis. Given the increasing emphasis on sustainability, this paper also explores various compact AI models suitable for edge devices, their deep compression techniques, and the deployment of these models using federated machine learning. These models are evaluated based on Green AI criteria, such as training time, floating-point operations, and model size to identify the most optimally performing model. The study also discusses data pre-processing techniques, model compression methods, and deployment strategies on edge devices like the Raspberry Pi. The results from this study highlight the potential of using AI in structural health monitoring systems, offering potential solutions that are not only effective but also environmentally sustainable. Through the development and deployment of these compact models, the study sets the stage for the future of reliable and sustainable infrastructural health monitoring.
author2 Fu Yuguang
author_facet Fu Yuguang
Chen, John Yong Han
format Final Year Project
author Chen, John Yong Han
author_sort Chen, John Yong Han
title Smart sensing with edge intelligence for autonomous structural health monitoring
title_short Smart sensing with edge intelligence for autonomous structural health monitoring
title_full Smart sensing with edge intelligence for autonomous structural health monitoring
title_fullStr Smart sensing with edge intelligence for autonomous structural health monitoring
title_full_unstemmed Smart sensing with edge intelligence for autonomous structural health monitoring
title_sort smart sensing with edge intelligence for autonomous structural health monitoring
publisher Nanyang Technological University
publishDate 2023
url https://hdl.handle.net/10356/172429
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