izpis_h1_title_alt

Data mining for fault diagnostics : a case for plastic injection molding
Kozjek, Dominik (Author), Vrabič, Rok (Author), Kralj, David (Author), Butala, Peter (Author), Lavrač, Nada (Author)

.pdfPDF - Presentation file, Download (947,08 KB)
URLURL - Source URL, Visit https://www.sciencedirect.com/science/article/pii/S2212827119305098 This link opens in a new window

Abstract
In manufacturing processes the automated identification of faulty operating conditions that might lead to insufficient product quality and reduced availability of the equipment is an important and challenging task. This paper proposes a data mining approach to the identification of complex faults, i.e. unplanned machine stops in plastic injection molding. Several data mining methods are considered, with a focus on the abilities to reveal patterns of faulty operating conditions and on the interpretation of the induced models with the objective to find the data mining method that best corresponds to the nature of the plastic-injection-molding process and the related data. Well-known data mining methods, i.e. J48, random forests, JRip rules, naïve Bayes, and k-nearest neighbors are applied to real industrial data. The results show that tested data mining methods can be effectively used to reveal patterns related to faulty operating conditions. The interpretation capacity of the tested methods, their ability to describe the operating conditions, and to reveal patterns related to faulty operating conditions, are demonstrated and discussed.

Language:English
Keywords:fault diagnostics, plastic injection molding, data analytics, data mining, industrial data
Work type:Article (dk_c)
Tipology:1.08 - Published Scientific Conference Contribution
Organization:FS - Faculty of Mechanical Engineering
Year:2019
Number of pages:f. 809-814
Numbering:Vol. 81
UDC:658.5.012.7:681.5(045)
ISSN on article:2212-8271
DOI:10.1016/j.procir.2019.03.204 Link is opened in a new window
COBISS.SI-ID:16687643 Link is opened in a new window
Views:139
Downloads:169
Metadata:XML RDF-CHPDL DC-XML DC-RDF
 
Average score:(0 votes)
Your score:Voting is allowed only to logged in users.
:
Share:AddThis
AddThis uses cookies that require your consent. Edit consent...

Record is a part of a proceedings

Title:52nd CIRP Conference on Manufacturing Systems (CMS), Ljubljana, Slovenia, June 12-14, 2019
COBISS.SI-ID:16674843 This link opens in a new window

Record is a part of a journal

Title:Procedia CIRP
Publisher:Elsevier
ISSN:2212-8271
COBISS.SI-ID:12981019 This link opens in a new window

Document is financed by a project

Funder:ARRS - Agencija za raziskovalno dejavnost Republike Slovenije (ARRS)
Project no.:P2-0270
Name:Proizvodni sistemi, laserske tehnologije in spajanje materialov

Secondary language

Language:Slovenian
Keywords:diagnosticiranje napak, injekcijsko brizganje plastike, analitika podatkov, podatkovno rudarjenje, industrijski podatki

Similar documents

Similar works from RUL:
Similar works from other Slovenian collections:

Comments

Leave comment

You have to log in to leave a comment.

Comments (0)
0 - 0 / 0
 
There are no comments!

Back