Unsupervised Anomalous Sound Detection for Mechanical Equipment Based on Domain Generalization Techniques

Monitoring mechanical equipment is crucial for industrial efficiency and safety. Anomalous sound detection faces challenges due to limited labeled data and varying acoustic conditions. Supervised methods require extensive labeled data, which is often scarce. Unsupervised methods, which model the distributions of normal data, are more suitable when normal data is abundant but anomalies are rare. In this paper, we present SAM-AE-GAN, an unsupervised anomaly detection model that combines self-attention, autoencoders, GANs, and spectral perturbation for data augmentation. The self-attention mechanism captures long-range dependencies, autoencoders enable efficient data representation, and GANs enhance generative performance. Spectral perturbation enhances adaptability, improving robustness and accuracy. Experimental results demonstrate the effectiveness of SAM-AE-GAN across five machine types in the 2022 Challenge TASK 2 dataset, validating its capability in complex industrial environments. This model provides an effective solution for anomaly detection and predictive maintenance, even in scenarios with limited labeled data.