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

Egileak: David Velásquez Rendón

Data: 13.07.2022

IEEE Access


Abstract

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 Machine Learning. This paper proposes a hybrid machine-learning ensemble real-time anomaly-detection pipeline that combines three Machine Learning models–Local Outlier Factor, One-Class Support Vector Machine, and Autoencoder–, through a weighted average to improve anomaly detection. The ensemble model was tested with three air-blowing machines obtaining a F1 -score value of 0.904, 0.890, and 0.887, respectively. The results of the ensemble model showed improved performance metrics concerning the individual metrics. A novelty of this model is that it consists of two stages inspired by a standard industrial system: i) a manufacturing stage and ii) an operation stage.

BIB_text

@Article {
author = {David Velásquez Rendón},
title = {A Hybrid Machine-Learning Ensemble for Anomaly Detection in Real-Time Industry 4.0 Systems},
journal = {IEEE Access},
volume = {10},
keywds = {
Anomaly Detection, Industry 4.0, Machine Learning, Predictive Maintenance, Real-Time
}
abstract = {

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 Machine Learning. This paper proposes a hybrid machine-learning ensemble real-time anomaly-detection pipeline that combines three Machine Learning models–Local Outlier Factor, One-Class Support Vector Machine, and Autoencoder–, through a weighted average to improve anomaly detection. The ensemble model was tested with three air-blowing machines obtaining a F1 -score value of 0.904, 0.890, and 0.887, respectively. The results of the ensemble model showed improved performance metrics concerning the individual metrics. A novelty of this model is that it consists of two stages inspired by a standard industrial system: i) a manufacturing stage and ii) an operation stage.


}
doi = {10.1109/ACCESS.2022.3188102},
date = {2022-07-13},
}
Vicomtech

Gipuzkoako Zientzia eta Teknologia Parkea,
Mikeletegi Pasealekua 57,
20009 Donostia / San Sebastián (Espainia)

+(34) 943 309 230

Zorrotzaurreko Erribera 2, Deusto,
48014 Bilbo (Espainia)

close overlay

Jokaeraren araberako publizitateko cookieak beharrezkoak dira eduki hau kargatzeko

Onartu jokaeraren araberako publizitateko cookieak