Anomaly detection for industrial quality assurance: A comparative evaluation of unsupervised deep learning models
Authors:
- Justus ZipfelJZJustus Zipfel
- Felix VerwornerFVFelix Verworner
- Marco FischerMFMarco Fischer
- Uwe WielandUWUwe Wieland
- Mathias KrausMKMathias Kraus
- Patrick ZschechPZPatrick Zschech
Open Access
- 31 January 2023
- journal article
- research article
- Published by Elsevier in Computers & Industrial Engineering
- Vol. 177
Abstract
No abstract availableKeywords
This publication has 29 references indexed in Scilit:
- Deep learning in business analytics and operations research: Models, applications and managerial implicationsEuropean Journal of Operational Research, 2020
- Segmentation-based deep-learning approach for surface-defect detectionJournal of Intelligent Manufacturing, 2019
- Prognostic Model Development with Missing LabelsBusiness & Information Systems Engineering, 2019
- Automatic Metallic Surface Defect Detection and Recognition with Convolutional Neural NetworksApplied Sciences, 2018
- Computer vision and deep learning–based data anomaly detection method for structural health monitoringStructural Health Monitoring, 2018
- Deep learningNature, 2015
- Facility location: concepts, models, algorithms and case studies. Series: Contributions to Management ScienceInternational Journal of Geographical Information Science, 2011
- Anomaly detectionACM Computing Surveys, 2009
- Applying wavelets transform and support vector machine for copper clad laminate defects classificationComputers & Industrial Engineering, 2009
- Nearest neighbor pattern classificationIEEE Transactions on Information Theory, 1967