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Deep Convolutional Clustering-Based Time Series Anomaly Detection
2021
Sensors
This paper presents a novel approach for anomaly detection in industrial processes. The system solely relies on unlabeled data and employs a 1D-convolutional neural network-based deep autoencoder architecture. As a core novelty, we split the autoencoder latent space in discriminative and reconstructive latent features and introduce an auxiliary loss based on k-means clustering for the discriminatory latent variables. We employ a Top-K clustering objective for separating the latent space,
doi:10.3390/s21165488
pmid:34450930
pmcid:PMC8400863
fatcat:6bk4djhthnd2bbx7kvb4gnxyxa