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This paper presents a novel Deep Autoencoder Anomaly Detection Model (DAADM) for detecting anomalies in aeroengines, addressing challenges such as high noise and imbalanced data. DAADM integrates adversarial training strategies and deep feature extraction to enhance the detection of minor anomalies, achieving superior performance compared to existing methods. Experimental results demonstrate that DAADM significantly improves accuracy, recall, and F1 scores, while also exhibiting strong anti-noise capabiliti

Author

叶明生

Language

EN