Continuous Process Monitoring Through Ensemble-Based Anomaly Detection

Deuse, J; Wiegand, M; Weisner, K

Continuous Process Monitoring Through Ensemble-Based Anomaly Detection

Deuse, J Wiegand, M Weisner, K

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Publisher:: Springer International Publishing
Publication Type:: Chapter
Citation:: Studies in Classification, Data Analysis, and Knowledge Organization, 2019, pp. 289-301
Issue Date:: 2019-01-01

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Field	Value	Language
dc.contributor.author	Deuse, J
dc.contributor.author	Wiegand, M
dc.contributor.author	Weisner, K
dc.date.accessioned	2020-06-15T22:17:25Z
dc.date.available	2020-06-15T22:17:25Z
dc.date.issued	2019-01-01
dc.identifier.citation	Studies in Classification, Data Analysis, and Knowledge Organization, 2019, pp. 289-301
dc.identifier.isbn	9783030251468
dc.identifier.uri	http://hdl.handle.net/10453/141433
dc.description.abstract	© Springer Nature Switzerland AG 2019. In many production processes, a complete quality inspection of all products is not feasible due to technological and organizational restrictions. In order to ensure zero-defect products, monitoring process parameters in real time and using them to predict product quality by supervised learning methods is a very established approached. However, this approach requires a joining of process parameters and quality features. In order to guarantee high-quality products even in the absence of traceability, a continuous process monitoring approach based on an anomaly detection ensemble method is beneficial.
dc.language	en
dc.publisher	Springer International Publishing
dc.relation.ispartof	Studies in Classification, Data Analysis, and Knowledge Organization
dc.relation.isbasedon	10.1007/978-3-030-25147-5_18
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Continuous Process Monitoring Through Ensemble-Based Anomaly Detection
dc.type	Chapter
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
dc.date.updated	2020-06-15T22:17:22Z
pubs.publication-status	Published

Abstract:

© Springer Nature Switzerland AG 2019. In many production processes, a complete quality inspection of all products is not feasible due to technological and organizational restrictions. In order to ensure zero-defect products, monitoring process parameters in real time and using them to predict product quality by supervised learning methods is a very established approached. However, this approach requires a joining of process parameters and quality features. In order to guarantee high-quality products even in the absence of traceability, a continuous process monitoring approach based on an anomaly detection ensemble method is beneficial.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/141433