WFRF:(Lejon Erik)
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Machine learning for detection of anomalies in press-hardening : Selection of efficient methods
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- Lejon, Erik (author)
- Gestamp HardTech AB
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- Kyösti, Petter (author)
- Luleå tekniska universitet,Signaler och system,ProcessIT Innovations R&D Centre
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- Lindström, John (author)
- Luleå tekniska universitet,Signaler och system,ProcessIT Innovations R&D Centre
- (creator_code:org_t)
- Elsevier, 2018
- 2018
- English.
- In: Procedia CIRP. - : Elsevier. - 2212-8271. ; 72, s. 1079-1083
- Journal article (peer-reviewed)
Abstract
Subject headings
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- The paper addresses machine learning methods, utilizing data from industrial control systems, that are suitable for detecting anomalies in the press-hardening process of automotive components. The paper is based on a survey of methods for anomaly detection in various applications. Suitable methods for the press-hardening process are implemented and evaluated. The result shows that it is possible to implement machine learning for anomaly detection by non-machine learning experts utilizing readily available programming libraries/APIs. The three evaluated methods for anomaly detection in the press-hardening process all perform well, with the autoencoder neural network scoring highest in the evaluation.
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Reglerteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Control Engineering (hsv//eng)
Keyword
- Reglerteknik
- Control Engineering
Publication and Content Type
- ref (subject category)
- art (subject category)
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- Luleå University of Technology
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