A Hybrid Machine-Learning Ensemble for Anomaly Detection in Real-Time Industry 4.0 Systems

A Hybrid Machine-Learning Ensemble for Anomaly Detection in Real-Time Industry 4.0 Systems

Detecting faults and anomalies in real-time industrial systems is a challenge due to the difficulty of sufficiently covering an industrial system’s complexity. Today, Industry 4.0 makes it possible to tackle these problems through emerging technologies such as the Internet of Things and M...

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Bibliographic Details
Main Authors: David Velasquez, Enrique Perez, Xabier Oregui, Arkaitz Artetxe, Jorge Manteca, Jordi Escayola Mansilla, Mauricio Toro, Mikel Maiza, Basilio Sierra
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Access
Subjects:
Anomaly detection
industry 40
machine learning
predictive maintenance
real-time
Online Access:https://ieeexplore.ieee.org/document/9813703/

Internet

https://ieeexplore.ieee.org/document/9813703/

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