Napovedovanje odpovedi izdelkov z metodami globokega učenja

diplomsko delo univerzitetnega študija Organizacija in management informacijskih sistemov

Blaž Sašek (Author), Davorin Kofjač (Mentor), Andrej Škraba (Co-mentor)

Abstract

Diplomsko delo obravnava razvoj in optimizacijo modelov za analizo garancijskih podatkov in napovedovanje odpovedi z metodami globokega učenja. Globoko učenje je redko uporabljeno v tovrstne namene, zato so raziskave na tem področju pomembne, a obenem težavne, saj obstaja manj predhodnih virov, s katerimi si lahko pomagamo. Na drugi strani pa se tehnologija v zadnjih letih razvija izjemno hitro, tako da lahko modele globokega učenja implementiramo tudi brez detajlnega poznavanja vseh elementov globokega učenja, kar je omogočilo razcvet uporabe in aplikacijo globokega učenja na široko paleto problemov. V nalogi smo preizkusili več različnih modelov, od prilagojenega enoslojnega perceptrona do konvolucijske nevronske mreže, in večje število optimizacijskih metod. Z uporabljenimi metodami smo dosegli 30–40-% stopnjo natančnosti, kar odstopa od želene 10-% stopnje napake. Pri tem moramo upoštevati majhen nabor vhodnih podatkov. Metode globokega učenja se ob zastavljenem zahtevnem pogoju niso izkazale kot primerne za uporabo, iz pridobljenih informacij pa zaključujemo, da bodo metode najverjetneje uporabne v prihodnje, ko bo na voljo več podatkov, ki bodo tudi bolj kvalitetni.

Keywords

garancijski podatki;strojno učenje;nevronske mreže;

Data

Language:	Slovenian
Year of publishing:	2017
Typology:	2.11 - Undergraduate Thesis
Organization:	UM FOV - Faculty of Organizational Sciences
Publisher:	[B. Sašek]
UDC:	004
COBISS:	7962387
Views:	1308
Downloads:	166
Average score:	0 (0 votes)
Metadata:

Other data

Secondary language:	English
Secondary title:	Predicting the failures of products using deep learning methods
Secondary abstract:	The thesis deals with the development and optimization of models for analyzing warranty data and predicting failure with deep learning methods. Deep learning is rarely used for such purposes, which is why research in this field is important but difficult, given that there are fewer sources we can rely on for previous experience. On the other hand, technology has been developing rapidly in recent years, so we are able to implement deep learning even without detailed knowledge of all the elements of the technology. This enabled the increase in use and application of deep learning to a wide range of problems. In the thesis, we tested several different models from the adapted single-layer perceptron to the convolutional neural network and a number of optimization methods. With the methods used, we achieved a 30-40% accuracy rate, which deviates from the desired error rate of 10%. However, we need to take into consideration a small set of input data we had available. The methods of deep learning did not prove to be suitable for use in this specific problem set, but from the acquired information we can conclude that the methods will most likely be useful in the future, when more data of higher quality is available.
Secondary keywords:	Warranty data;Machine learning;Neural networks;Deep learning;Python;Tensorflow;
URN:	URN:SI:UM:
Type (COBISS):	Bachelor thesis/paper
Thesis comment:	Univ. v Mariboru, Fak. za organizacijske vede
Pages:	61 f.
ID:	10862151