master's thesis
David Nabergoj (Author), Erik Štrumbelj (Mentor)

Abstract

We assess the usability of Bayesian deep learning methods for remaining useful life estimation. To compare Bayesian models to standard deep learning models, we propose a model evaluation method based on simulated device maintenance. We find that Bayesian models outperform their architecturally equivalent deep learning models on synthetic data as well as on two benchmark datasets. The proposed evaluation method is relevant for practical applications and research, as it directly estimates maintenance costs and allows for more easily interpretable model comparisons.

Keywords

predictive maintenance;remaining useful life;Bayesian deep learning;model evaluation;computer science;computer and information science;master's thesis;

Data

Language: English
Year of publishing:
Typology: 2.09 - Master's Thesis
Organization: UL FRI - Faculty of Computer and Information Science
Publisher: [D. Nabergoj]
UDC: 004(043.2)
COBISS: 84824579 Link will open in a new window
Views: 237
Downloads: 74
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Other data

Secondary language: Slovenian
Secondary title: Napovedovalno vzdrževanje z Bayesovim globokim učenjem
Secondary abstract: V tej nalogi ocenimo uporabnost metod Bayesovega globokega učenja za ocenjevanje preostale dobe koristnosti naprav. Predlagamo metodo za vrednotenje modelov, ki temelji na simuliranem vzdrževanju naprav. Na podlagi eksperimentov z umetnimi podatki in dvema referenčnima podatkovnima množicama ugotovimo, da so Bayesovi modeli boljši kot standardni globoki modeli z enako arhitekturo. Predlagana metoda za vrednotenje je relevantna v praktičnih aplikacijah in raziskovanju, saj neposredno oceni stroške vzdrževanja in omogoča interpretabilno primerjavo modelov.
Secondary keywords: napovedovalno vzdrževanje;preostala doba koristnosti;Bayesovo globoko učenje;vrednotenje modelov;računalništvo in informatika;magisteriji;Računalništvo;Univerzitetna in visokošolska dela;
Type (COBISS): Master's thesis/paper
Study programme: 1000471
Thesis comment: Univ. v Ljubljani, Fak. za računalništvo in informatiko
Pages: XII, 82 str.
ID: 13918264