Unsupervised Deep Anomaly Detection for Industrial Multivariate Time Series Data

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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Bibliographic Details
Main Authors: Wenqiang Liu, Li Yan, Ningning Ma, Gaozhou Wang, Xiaolong Ma, Peishun Liu, Ruichun Tang
Format: Article
Language:English
Published: MDPI AG 2024-01-01
Series:Applied Sciences
Subjects:
deep learning
anomaly detection
convolutional autoencoder
attention mechanism
unsupervised learning
Online Access:https://www.mdpi.com/2076-3417/14/2/774

Internet

https://www.mdpi.com/2076-3417/14/2/774

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