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Machine Learning Algorithms for Classifying Component Defects for Predictive Maintenance

  • Reducing costs is an important part in todays business. Therefore manufacturers try to reduce unnecessary work processes and storage costs. Machine maintenance is a big, complex, regular process. In addition, the spare parts required for this must be kept in stock until a machine fails. In order to avoid a production breakdown in the event of an unexpected failure, more and more manufacturers rely on predictive maintenance for their machines. This enables more precise planning of necessary maintenance and repair work, as well as a precise ordering of the spare parts required for this. A large amount of past as well as current information is required to create such a predictive forecast about machines. With the classification of motors based on vibration, this paper deals with the implementation of predictive maintenance for thermal systems. There is an overview of suitable sensors and data processing methods, as well as various classification algorithms. In the end, the best sensor-algorithm combinations are shown.

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Metadaten
Author:Christoph Kammerer
DOI:https://doi.org/10.48446/opus-12291
ISSN:1437-7624
Parent Title (German):26. Interdisziplinäre Wissenschaftliche Konferenz Mittweida
Publisher:Hochschule Mittweida
Place of publication:Mittweida
Document Type:Conference Proceeding
Language:English
Year of Completion:2021
Publishing Institution:Hochschule Mittweida
Contributing Corporation:Hochschule Heilbronn
Release Date:2021/05/17
Tag:Industrial Internet; Predictive maintenance
GND Keyword:Industrie 4.0; Internet der Dinge; Big Data
Issue:002
Page Number:5
First Page:149
Last Page:153
Open Access:Frei zugänglich
Licence (German):License LogoEs gilt das UrhG