Deep Convolutional Clustering-Based Time Series Anomaly Detection

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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Bibliographic Details
Main Authors: Gavneet Singh Chadha, Intekhab Islam, Andreas Schwung, Steven X. Ding
Format: Article
Language:English
Published: MDPI AG 2021-08-01
Series:Sensors
Subjects:
unsupervised learning
deep convolutional autoencoder
top-K K-means clustering
anomaly detection
Online Access:https://www.mdpi.com/1424-8220/21/16/5488

Internet

https://www.mdpi.com/1424-8220/21/16/5488

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