NAPOVEDOVANJE ODPOVEDI IZDELKOV Z METODAMI STROJNEGA UČENJA

Mujanović, Amira

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Title:	NAPOVEDOVANJE ODPOVEDI IZDELKOV Z METODAMI STROJNEGA UČENJA
Authors:	ID Mujanović, Amira (Author) ID Kofjač, Davorin (Mentor) More about this mentor... ID Škraba, Andrej (Comentor)
Files:	MAG_Mujanovic_Amira_2016.pdf (1,27 MB) MD5: 16A70F5578A199044239B324AADF8334
Language:	Slovenian
Work type:	Master's thesis/paper
Organization:	FOV - Faculty of Organizational Sciences in Kranj
Abstract:	Magistrska naloga obravnava razvoj modela za napovedovanje odpovedi izdelkov v garancijski dobi. Z odpovedovanjem izdelkov in problematiko zagotavljanja popravil v garancijski dobi se soočajo vsa proizvodna podjetja. Zagotavljanje popravil v garancijskem roku podjetjem predstavlja strošek, ki ga poskušajo minimizirati s pomočjo predvidevanja deležev odpovedi. Najpogosteje se napovedi izvedejo z empiričnimi modeli, ki so zgrajeni na preteklih podatkih o podobnih izdelkih in prilagojeni glede na izkušnje. V sklopu magistrske naloge smo s pomočjo različnih metod strojnega učenja in realnih podatkov razvili napovedni model in ocenili uspešnost napovedovanja. Najboljše rezultate napovedovanja smo dobili pri ansamblih regresijskih dreves, pri katerih smo podatke prilagodili eksponentnem modelu. Za zaključek smo pripravili priporočila kateri model uporabiti ob omejenem poznavanju podatkov o odpovedih.
Keywords:	Napovedni model, odpoved izdelka, garancijski rok, kakovost, strojno učenje, nevronske mreže, regresijska drevesa.
Place of publishing:	Maribor
Year of publishing:	2016
PID:	20.500.12556/DKUM-57638
COBISS.SI-ID:	7554067
NUK URN:	URN:SI:UM:DK:T4YSYIU8
Publication date in DKUM:	01.04.2016
Views:	1913
Downloads:	202
Metadata:
Categories:	FOV
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Secondary language

Language:	English
Title:	PRODUCT FAILURE PREDICTION WITH MACHINE LEARNING METHODS
Abstract:	Master's thesis deals with the development of a model for predicting the failure of products during the warranty period. All of the manufacturing companies are facing the product failure problem and problem with offering the possibility of repairing those products. Providing guarantees represents costs, which companies are trying to minimize by predicting the failure rates. Most often, this is done with empirical models, which are built on historical data for similar products and customized based on experiences. As part of the master's thesis, we developed different models using various methods of machine learning and real data. After development, we assessed the quality of prediction for each model. Ensembles of regression trees obtained the best results; in that case, the data was fitted to the exponential model. To wind up, we prepared recommendations which model to use in different scenarios.
Keywords:	Prediction model, product failure, warranty period, quality, machine learning, neural networks, regression trees.

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