Real-Time Deep Anomaly Detection Framework for Multivariate Time-Series Data in Industrial IoT

The data produced by millions of connected devices and smart sensors in the Industrial Internet of Things (IIoT) is highly dynamic, large-scale, heterogeneous, and time-stamped. These time-stamped data are the core of IIoT automation and have the potential to affect industrial processes intensely. I...

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Bibliographic Details
Published in:	IEEE sensors journal Vol. 22; no. 23; pp. 22836 - 22849
Main Authors:	Nizam, Hussain, Zafar, Samra, Lv, Zefeng, Wang, Fan, Hu, Xiaopeng
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.12.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Anomalies Anomaly detection Artificial neural networks Automation deep learning (DL) Feature extraction Image edge detection Industrial applications Industrial Internet of Things Industrial Internet of Things (IIoT) Intelligent sensors Internet of Things long short-term memory (LSTM) autoencoder (AE) machine learning (ML) Multivariate analysis multivariate time series rare event detection Real time Real-time systems sensor data Smart sensors Time series Training
ISSN:	1530-437X, 1558-1748
Online Access:	Get full text
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