Unsupervised Deep Anomaly Detection for Industrial Multivariate Time Series Data

With the rapid development of deep learning, researchers are actively exploring its applications in the field of industrial anomaly detection. Deep learning methods differ significantly from traditional mathematical modeling approaches, eliminating the need for intricate mathematical derivations and...

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Bibliografiset tiedot
Päätekijät:	Wenqiang Liu, Li Yan, Ningning Ma, Gaozhou Wang, Xiaolong Ma, Peishun Liu, Ruichun Tang
Aineistotyyppi:	Artigo
Kieli:	Inglês
Julkaistu:	MDPI AG 2024-01-01
Sarja:	Applied Sciences
Aiheet:	deep learning anomaly detection convolutional autoencoder attention mechanism unsupervised learning
Linkit:	https://www.mdpi.com/2076-3417/14/2/774
Tagit:	Lisää tagi Ei tageja, Lisää ensimmäinen tagi!

Unsupervised Deep Anomaly Detection for Industrial Multivariate Time Series Data

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