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This paper presents an offline anomaly detection framework using an LSTM autoencoder for Electro-Hydrostatic Actuators (EHAs), achieving an average accuracy of 99.0% and high recall on sensor data, outperforming classical methods.
TPA-AD is a two-stage pseudo anomaly-guided method for bearing time-series anomaly detection that generates pseudo-anomalous windows near normal boundaries using reconstruction models and contrastive learning, then scores anomalies with KNN—without requiring real anomaly samples during training. It is evaluated on bearing fault and degradation datasets, including high-speed train axle-box bearing data.
This paper introduces CAFD, a learning-based approach for DNN fault detection that integrates model-based, distance-based, and a novel concept-based feature called Concept Failure Ratio (CFR) derived from Vision-Language Models. CAFD consistently outperforms state-of-the-art baselines in fault detection rate across multiple datasets and budgets.