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Article

Isolation Forests and Deep Autoencoders for Industrial Screw Tightening Anomaly Detection

1
ALGORITMI R&D Centre, Department of Information Systems, University of Minho, 4804-533 Guimarães, Portugal
2
Bosch Car Multimedia, 4705-820 Braga, Portugal
3
EPMQ-IT CCG ZGDV Institute, 4804-533 Guimarães, Portugal
*
Author to whom correspondence should be addressed.
Academic Editor: Osvaldo Gervasi
Computers 2022, 11(4), 54; https://doi.org/10.3390/computers11040054
Received: 24 February 2022 / Revised: 31 March 2022 / Accepted: 6 April 2022 / Published: 8 April 2022
(This article belongs to the Special Issue Selected Papers from ICCSA 2021)
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 labeling is costly, requiring a manual effort, we focus on unsupervised detection approaches. In particular, we assume a computationally light low-dimensional problem formulation based on angle–torque pairs. Our work is focused on two unsupervised machine learning (ML) algorithms: isolation forest (IForest) and a deep learning autoencoder (AE). Several computational experiments were held by assuming distinct datasets and a realistic rolling window evaluation procedure. First, we compared the two ML algorithms with two other methods, a local outlier factor method and a supervised Random Forest, on older data related with two production days collected in November 2020. Since competitive results were obtained, during a second stage, we further compared the AE and IForest methods by adopting a more recent and larger dataset (from February to March 2021, totaling 26.9 million observations and related to three distinct assembled products). Both anomaly detection methods obtained an excellent quality class discrimination (higher than 90%) under a realistic rolling window with several training and testing updates. Turning to the computational effort, the AE is much lighter than the IForest for training (around 2.7 times faster) and inference (requiring 3.0 times less computation). This AE property is valuable within this industrial domain since it tends to generate big data. Finally, using the anomaly detection estimates, we developed an interactive visualization tool that provides explainable artificial intelligence (XAI) knowledge for the human operators, helping them to better identify the angle–torque regions associated with screw tightening failures. View Full-Text
Keywords: autoencoder; deep learning; Industry 4.0; isolation forest; one-class classification; unsupervised learning autoencoder; deep learning; Industry 4.0; isolation forest; one-class classification; unsupervised learning
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MDPI and ACS Style

Ribeiro, D.; Matos, L.M.; Moreira, G.; Pilastri, A.; Cortez, P. Isolation Forests and Deep Autoencoders for Industrial Screw Tightening Anomaly Detection. Computers 2022, 11, 54. https://doi.org/10.3390/computers11040054

AMA Style

Ribeiro D, Matos LM, Moreira G, Pilastri A, Cortez P. Isolation Forests and Deep Autoencoders for Industrial Screw Tightening Anomaly Detection. Computers. 2022; 11(4):54. https://doi.org/10.3390/computers11040054

Chicago/Turabian Style

Ribeiro, Diogo, Luís M. Matos, Guilherme Moreira, André Pilastri, and Paulo Cortez. 2022. "Isolation Forests and Deep Autoencoders for Industrial Screw Tightening Anomaly Detection" Computers 11, no. 4: 54. https://doi.org/10.3390/computers11040054

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