Abstract

This study proposes an ensemble denoising auto-encoder-based dynamic threshold (EDAE-DT) to overcome the false alarm issue in anomaly detection. The proposed ensemble denoising auto-encoder can model the normal condition well through a denoising task and an ensemble technique. The dynamic threshold sets a time-varying threshold that considers the variation of normal data. Performance metrics for anomaly detection are newly proposed to quantitatively verify the performance. A new sensitivity is defined from the dynamic threshold to identify which signal is related to the change that arises due to an anomaly. The diagnostic performance of the proposed approach is compared using metrics for classification and a confusion matrix. Validation results, which examined thermal power plant datasets, show that the proposed modeling method outperforms both the auto-encoder and denoising auto-encoder approaches. Additionally, the proposed method can significantly reduce the false alarm rate, as compared to conventional methods, while detecting anomalies faster than experts. The anomaly-related signals are identified successfully through the newly defined sensitivity. Finally, the diagnositc results demonstrate that the proposed approach is more accurate than conventional methods.