Isolation Forests and Deep Autoencoders for Industrial Screw Tightening Anomaly Detection

Isolation Forests and Deep Autoencoders for Industrial Screw Tightening Anomaly Detection

Within the context of Industry 4.0, quality assessment procedures using data-driven techniques are becoming more critical due to the generation of massive amounts of production data. In this paper, we address the detection of abnormal screw tightening processes, which is a key industrial task. Since...

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Bibliographic Details
Main Authors:	Diogo Ribeiro, Luís Miguel Matos, Guilherme Moreira, André Pilastri, Paulo Cortez
Format:	Article
Language:	English
Published:	MDPI AG 2022-04-01
Series:	Computers
Subjects:	autoencoder deep learning Industry 4.0 isolation forest one-class classification unsupervised learning
Online Access:	https://www.mdpi.com/2073-431X/11/4/54

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