Napovedovanje odpovedi izdelkov z metodami globokega učenja

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Title:	Napovedovanje odpovedi izdelkov z metodami globokega učenja
Authors:	ID Sašek, Blaž (Author) ID Kofjač, Davorin (Mentor) More about this mentor... ID Škraba, Andrej (Comentor)
Files:	UN_Sasek_Blaz_2017.pdf (3,16 MB) MD5: 7089AB4CEB90D87B0CA1784AB0459EA8 PID: 20.500.12556/dkum/dc51ca82-4c01-49ee-b78d-bc21922a1a59
Language:	Slovenian
Work type:	Bachelor thesis/paper
Organization:	FOV - Faculty of Organizational Sciences in Kranj
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, Globoko učenje, Python, Tensorflow
Place of publishing:	Kranj
Year of publishing:	2017
PID:	20.500.12556/DKUM-67936
COBISS.SI-ID:	7962387
NUK URN:	URN:SI:UM:DK:2RKNENYB
Publication date in DKUM:	14.09.2017
Views:	5058
Downloads:	808
Metadata:
Categories:	FOV
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Licences

License:	CC BY-NC-SA 4.0, Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International

Link:	http://creativecommons.org/licenses/by-nc-sa/4.0/
Description:	A Creative Commons license that bans commercial use and requires the user to release any modified works under this license.
Licensing start date:	04.09.2017

Secondary language

Language:	English
Title:	Predicting the failures of products using deep learning methods
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.
Keywords:	Warranty data, Machine learning, Neural networks, Deep learning, Python, Tensorflow

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