Mid Sweden University

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.