Data mining for fault diagnostics

ǂa ǂcase for plastic injection molding

Dominik Kozjek (Author), Rok Vrabič (Author), David Kralj (Author), Peter Butala (Author), Nada Lavrač (Author)

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.

Keywords

diagnosticiranje napak;injekcijsko brizganje plastike;analitika podatkov;podatkovno rudarjenje;industrijski podatki;fault diagnostics;plastic injection molding;data analytics;data mining;industrial data;

Data

Language:	English
Year of publishing:	2019
Typology:	1.08 - Published Scientific Conference Contribution
Organization:	IJS - Jožef Stefan Institute
UDC:	658.5.012.7:681.5(045)
COBISS:	16687643
ISSN:	2212-8271
Views:	860
Downloads:	564
Average score:	0 (0 votes)
Metadata:

Other data

Secondary language:	Slovenian
Secondary keywords:	diagnosticiranje napak;injekcijsko brizganje plastike;analitika podatkov;podatkovno rudarjenje;industrijski podatki;
Type (COBISS):	Article
Pages:	f. 809-814
Issue:	ǂVol. ǂ81
Chronology:	2019
DOI:	10.1016/j.procir.2019.03.204
ID:	11185805