On-Device Fault Diagnosis With Augmented Acoustic Emission Data: A Case Study on Carbon Fiber Panels

Acoustic Emission (AE)-based fault diagnosis in Structural Health Monitoring (SHM) systems faces challenges of data scarcity and model overfitting due to the complexity of AE data acquisition and the high cost of labeling. To address these issues, this study systematically explores various data augmentation techniques for AE signal processing and evaluates their impact on model robustness and accuracy. Furthermore, given the complexity of traditional machine learning (ML) models and their deployment challenges on resource-constrained embedded devices, we investigate lightweight ML algorithms and propose a Tiny Machine Learning (TinyML)-based fault diagnosis approach. Experimental validation on a carbon fiber panel fault diagnosis case demonstrates that the proposed method significantly improves classification performance under data scarce conditions while enabling real-time fault diagnosis on embedded systems. These findings underscore the potential of integrating data augmentation, lightweight ML algorithms, and TinyML to enhance both diagnostic accuracy and real-time performance in SHM applications.