Deep Convolutional Clustering-Based Time Series Anomaly Detection

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 reconstruct...

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Detalles Bibliográficos
Autores principales: Chadha, Gavneet Singh, Islam, Intekhab, Schwung, Andreas, Ding, Steven X.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8400863/
https://www.ncbi.nlm.nih.gov/pubmed/34450930
http://dx.doi.org/10.3390/s21165488

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8400863/
https://www.ncbi.nlm.nih.gov/pubmed/34450930
http://dx.doi.org/10.3390/s21165488

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