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Tiny Machine Learning for Damage Classification in Concrete Using Acoustic Emission Signals
Mid Sweden University, Faculty of Science, Technology and Media, Department of Computer and Electrical Engineering (2023-).
Mid Sweden University, Faculty of Science, Technology and Media, Department of Computer and Electrical Engineering (2023-).
Mid Sweden University, Faculty of Science, Technology and Media, Department of Computer and Electrical Engineering (2023-).ORCID iD: 0000-0001-9572-3639
Mid Sweden University, Faculty of Science, Technology and Media, Department of Computer and Electrical Engineering (2023-).ORCID iD: 0000-0002-8382-0359
2023 (English)In: 2023 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), IEEE, 2023Conference paper, Published paper (Refereed)
Abstract [en]

Acoustic emission (AE) is a widely used non-destructive test method in structural health monitoring applications to identify the damage type in the material. Usually, the analysis of the AE signal is done by using traditional parameter-based methods. Recently, machine learning methods showed promising results for the analysis of AE signals. However, these machine learning models are complex, slow, and consume significant amounts of energy. To address these limitations and to explore the trade-off between model complexity and the classification accuracy, this paper presents a lightweight artificial neural network model to classify damage types in concrete material using raw acoustic emission signals. The model consists of one hidden layer with four neurons and is trained on a public acoustic emission signal dataset. The created model is deployed to several microcontrollers and the performance of the model is evaluated and compared with a state-of-the-art machine learning model. The model achieves 98.4% accuracy on the test data with only 4019 parameters. In terms of evaluation metrics, the proposed tiny machine learning model outperforms previously proposed models 10 to 1000 times. The proposed model thus enables machine learning in real-time structural health monitoring applications. 

Place, publisher, year, edition, pages
IEEE, 2023.
Keywords [en]
acoustic emission, damage classification, embedded systems, IoT, machine learning, structural-health-monitoring, TinyML
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:miun:diva-49095DOI: 10.1109/I2MTC53148.2023.10175972ISI: 001039259600092Scopus ID: 2-s2.0-85166377110ISBN: 9781665453837 (electronic)OAI: oai:DiVA.org:miun-49095DiVA, id: diva2:1788856
Conference
2023 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)
Available from: 2023-08-17 Created: 2023-08-17 Last updated: 2024-05-13Bibliographically approved
In thesis
1. Tiny Machine Learning for Structural Health Monitoring with Acoustic Emissions
Open this publication in new window or tab >>Tiny Machine Learning for Structural Health Monitoring with Acoustic Emissions
2024 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Acoustic Emission (AE) technology, as one of the non-destructive Structural Health Monitoring (SHM) methods, is increasingly utilized for the damage prediction, classification, maintenance, and real-time monitoring of infrastructure. Addressing the need for low latency, power consumption and high portability, a novel approach has been adopted where processing algorithms are embedded close to the sensors on these devices. Continuous data monitoring and collection, coupled with data processing and interpretation comparable to human experts, are anticipated from the next generation of the Internet of Things and smart sensing systems. While Machine Learning (ML) and Deep Learning (DL) has been successfully applied in a number of domains including SHM, resource-constrained, low-power devices pose a challenge for computationally complex ML algorithm execution.

To explore the feasibility of deploying ML and DL algorithms on edge devices, this study first proposes a lightweight CNN model based on raw AE signals for concrete damage classification and evaluates its performance on an ultra-low-power microcontroller unit (MCU). Subsequently, to further simplify the algorithm and explore the adaptability across various MCU platforms, a raw AE signal-based Artificial Neural Network (ANN) model is proposed, and its deployment performance on multiple MCUs is assessed. Additionally, the study assesses the impact of feature extraction on ANN performance with raw AE signals on MCUs, finding that using raw data directly is more resource and time-efficient. Lastly, the study investigates the generalization ability of the aforementioned CNN on a carbon fiber panel AE dataset, as well as the performance of 13 traditional ML algorithms on this dataset and their final deployment performance on MCUs. Due to the small size of the dataset, various data augmentation methods were also introduced and their impact on model robustness and accuracy was evaluated.

This thesis demonstrates for the first time that real-time inference on edge devices using AE signals for SHM is feasible. It also effectively demonstrates how to balance the critical trade-offs between accuracy, resource demands, and power consumption. Different MCUs and signal preprocessing methods are evaluated, and the impact of various data augmentation techniques on the accuracy of different ML algorithms and their inference robustness is explored in response to the challenge of collecting AE data, which is crucial for the next generation of SHM devices.

Place, publisher, year, edition, pages
Sundsvall: Mid Sweden University, 2024. p. 48
Series
Mid Sweden University licentiate thesis, ISSN 1652-8948 ; 204
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:miun:diva-51322 (URN)978-91-89786-69-1 (ISBN)
Presentation
2024-06-13, C312, Holmgatan 10, Sundsvall, 13:00 (English)
Opponent
Supervisors
Note

Vid tidpunkten för framläggningen av avhandlingen var följande delarbeten opublicerade: delarbete 4 och 5 (inskickade manuskript).

At the time of the defence the following papers were unpublished: paper 4 and 5 (submitted manuscripts).

Available from: 2024-05-14 Created: 2024-05-13 Last updated: 2024-05-14Bibliographically approved

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Adin, VeysiZhang, YuxuanOelmann, BengtBader, Sebastian

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