Anomaly Detection in Resonance Patterns
Using variational autoencoders to detect anomalous resonance patterns.
Normal vs. Anomalous Resonance
In structural health monitoring, changes in resonance patterns can indicate damage, degradation, or other structural modifications. Anomaly detection identifies these deviations from normal operating conditions.
VAE-Based Approach
We train a Variational Autoencoder (VAE) on spectra from healthy systems. The VAE learns the distribution of normal resonance patterns. At inference time, spectra with high reconstruction error or low likelihood under the learned distribution are flagged as anomalous.
Results
On our benchmark dataset of structural vibration recordings, the VAE approach achieves 94% anomaly detection rate with a 3% false positive rate, outperforming traditional threshold-based methods.
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