ǂa ǂcase for machine-to-machine communication
Rok Vrabič (Author), Dominik Kozjek (Author), Peter Butala (Author)

Abstract

In most manufacturing processes the defect rate is very low. Sometimes, only a few parts per million are defective because of a faulty process. For this reason, fault diagnostics is faced with extremely imbalanced data sets and requires large volumes of data to achieve a reasonable performance. This paper explores whether a machine-to-machine approach can be used, in which several work systems share the process data to improve the accuracy of the fault-detection model. The model is based on machine learning and is applied to industrial data from approximately two million process cycles performed on several injection moulding work systems.

Keywords

manufacturing system;predictive model;machine-to-machine;

Data

Language: English
Year of publishing:
Typology: 1.01 - Original Scientific Article
Organization: UL FS - Faculty of Mechanical Engineering
UDC: 658.5(045)
COBISS: 15490587 Link will open in a new window
ISSN: 0007-8506
Views: 2112
Downloads: 519
Average score: 0 (0 votes)
Metadata: JSON JSON-RDF JSON-LD TURTLE N-TRIPLES XML RDFA MICRODATA DC-XML DC-RDF RDF

Other data

Secondary language: Slovenian
Secondary keywords: proizvodni sistemi;napovedni modeli;komunikacija stroj-stroj;
Embargo end date (OpenAIRE): 2020-06-18
Pages: str. 433-436
Volume: ǂVol. ǂ66
Issue: ǂiss. ǂ1
Chronology: 2017
DOI: 10.1016/j.cirp.2017.04.001
ID: 10941837
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