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Synthetizing acoustic emission data using a diffusion-based generative artificial intelligence model
Conference proceeding

Synthetizing acoustic emission data using a diffusion-based generative artificial intelligence model

Pedram Bazrafshan and Arvin Ebrahimkhanlou
Proceedings of SPIE, the international society for optical engineering, v 13952
16 Apr 2026
DOI: https://doi.org/10.1117/12.3090609

Abstract

This study investigates the use of denoising diffusion probabilistic models (DDPMs) to generate synthetic time-frequency images of acoustic emission (AE) signals. Conventional numerical simulations of AE signals are limited by their deterministic nature, producing identical outputs that fail to capture the stochastic variability observed in real materials. In this work, a dataset of 53,965 AE signals collected from concrete, wood, and recycled plastic specimens was transformed into time-frequency images for model training. The DDPM, implemented with a U-Net backbone, was trained for 600 epochs at 512×512 resolution using mean squared error loss and a linear noise schedule. The model introduces Gaussian noise during the forward process and incrementally removes it during the reverse process to reconstruct high-fidelity images. Evaluation with the Fréchet inception distance (FID) demonstrated a score of 41.2, confirming satisfactory alignment between the generated and real data distributions. The results show that the model effectively captured both coarse and fine features of AE signals, while maintaining variability consistent with real-world loading conditions and material behavior. These findings highlight the capability of DDPMs to provide robust synthetic datasets for AE research, supporting structural health monitoring and nondestructive evaluation.

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