Deep Learning Autoencoders for FFT-Based Clustering and Temporal Damage Evolution in Acoustic Emission Data from Composite Materials

Structural health monitoring (SHM) in Composite Fiber Reinforced Polymer (CFRP) structures is essential to ensure safety and reliability during service, particularly in critical industries such as aerospace and wind energy. Traditional methods of analysing Acoustic Emission (AE) signals in the time domain often fail to accurately detect subtle or early-stage damage, limiting their effectiveness. The present study introduces a novel approach that integrates frequency-domain analysis using Fast Fourier Transform (FFT) with deep learning techniques for more accurate and proactive damage detection. AE signals are first transformed into the frequency domain, where significant frequency components are extracted and used as input for an autoencoder network. The autoencoder reduces the dimensionality of the data while preserving essential features, enabling unsupervised clustering to identify distinct damage states. Temporal damage evolution is modeled using Markov chain analysis to provide insights into how damage progresses over time. The proposed method achieves a reconstruction error of 0.0017 and a high R-squared value of 0.95, indicating the autoencoder's effectiveness in learning compact representations while minimising information loss. Clustering results, with a silhouette score of 0.37, demonstrate well-separated clusters that correspond to different damage stages. Markov chain analysis captures the transitions between damage states, providing a predictive framework for assessing damage progression. These findings highlight the potential of the proposed approach for early damage detection and predictive maintenance, significantly improving the effectiveness of AE-based SHM systems in reducing downtime and extending component lifespan.