System failure estimation based on field data and
semi-parametric modeling

J 2019

System failure estimation based on field data and semi-parametric modeling

VALIŠ, David; Ondřej POKORA a Jan KOLÁČEK

Základní údaje

Originální název

System failure estimation based on field data and semi-parametric modeling

Autoři

VALIŠ, David; Ondřej POKORA a Jan KOLÁČEK

Vydání

Engineering Failure Analysis, Oxford, PERGAMON-ELSEVIER SCIENCE LTD, 2019, 1350-6307

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

50902 Social sciences, interdisciplinary

Stát vydavatele

Velká Británie a Severní Irsko

Utajení

není předmětem státního či obchodního tajemství

Odkazy

Full Text

Impakt faktor

Impact factor: 2.897

Označené pro přenos do RIV

Ano

Kód RIV

RIV/00216224:14310/19:00107351

Organizační jednotka

Přírodovědecká fakulta

Klíčová slova anglicky

Oil field data; Functional data analysis; Generalized additive models; Ornstein-Uhlenbeck process; First hitting time; Residual useful life

Štítky

rivok

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 17. 3. 2020 14:55, Mgr. Marie Novosadová Šípková, DiS.

Anotace

V originále

A top-priority task nowadays is to ensure quality, safety, and dependability of technical systems. As present systems are highly reliable, it is relatively unlikely for hard failure to occur frequently. One of the ways to avoid failures is by monitoring the conditions and degradation of the system using diagnostic signals. In this article, modern and nontrivial semiparametric approaches to analyze the statistically relevant set of field data are used. In particular, the generalized additive models (GAM) are applied. GAM reflect the current trends in statistics as they include both linear and spline-based modeling. We applied GAM to successfully obtain an appropriate description of the variability of the analyzed field data. The analyzed data come as diagnostic signals from an observed vehicle fleet. Based on the diagnostic signals and applied GAM, we present outcomes from investigating, studying and modeling the technical condition, degradation and failure occurrence of the observed system.

Návaznosti

GJ17-22950Y, projekt VaV

Název: Statistická inference pro složité náhodné procesy v ekonometrickém modelování

Investor: Grantová agentura ČR, Statistická inference pro složité náhodné procesy v ekonometrickém modelování

Citovat

VALIŠ, David; Ondřej POKORA a Jan KOLÁČEK. System failure estimation based on field data and semi-parametric modeling. Engineering Failure Analysis. Oxford: PERGAMON-ELSEVIER SCIENCE LTD, 2019, roč. 101, JUL 2019, s. 473-484. ISSN 1350-6307. Dostupné z: https://doi.org/10.1016/j.engfailanal.2019.04.014.

@article{1520497,
   author = {Vališ, David and Pokora, Ondřej and Koláček, Jan},
   article_location = {Oxford},
   article_number = {JUL 2019},
   doi = {https://doi.org/10.1016/j.engfailanal.2019.04.014},
   keywords = {Oil field data; Functional data analysis; Generalized additive models; Ornstein-Uhlenbeck process; First hitting time; Residual useful life},
   language = {eng},
   issn = {1350-6307},
   journal = {Engineering Failure Analysis},
   title = {System failure estimation based on field data and semi-parametric modeling},
   url = {https://www.sciencedirect.com/science/article/pii/S1350630718310252},
   volume = {101},
   year = {2019}
}

TY  - JOUR
ID  - 1520497
AU  - Vališ, David - Pokora, Ondřej - Koláček, Jan
PY  - 2019
TI  - System failure estimation based on field data and semi-parametric modeling
JF  - Engineering Failure Analysis
VL  - 101
IS  - JUL 2019
SP  - 473-484
EP  - 473-484
PB  - PERGAMON-ELSEVIER SCIENCE LTD
SN  - 13506307
KW  - Oil field data
KW  - Functional data analysis
KW  - Generalized additive models
KW  - Ornstein-Uhlenbeck process
KW  - First hitting time
KW  - Residual useful life
UR  - https://www.sciencedirect.com/science/article/pii/S1350630718310252
L2  - https://www.sciencedirect.com/science/article/pii/S1350630718310252
N2  - A top-priority task nowadays is to ensure quality, safety, and dependability of technical systems. As present systems are highly reliable, it is relatively unlikely for hard failure to occur frequently. One of the ways to avoid failures is by monitoring the conditions and degradation of the system using diagnostic signals. In this article, modern and nontrivial semiparametric approaches to analyze the statistically relevant set of field data are used. In particular, the generalized additive models (GAM) are applied. GAM reflect the current trends in statistics as they include both linear and spline-based modeling. We applied GAM to successfully obtain an appropriate description of the variability of the analyzed field data. The analyzed data come as diagnostic signals from an observed vehicle fleet. Based on the diagnostic signals and applied GAM, we present outcomes from investigating, studying and modeling the technical condition, degradation and failure occurrence of the observed system.
ER  -

VALIŠ, David; Ondřej POKORA a Jan KOLÁČEK. System failure estimation based on field data and semi-parametric modeling. \textit{Engineering Failure Analysis}. Oxford: PERGAMON-ELSEVIER SCIENCE LTD, 2019, roč.~101, JUL 2019, s.~473-484. ISSN~1350-6307. Dostupné z: https://doi.org/10.1016/j.engfailanal.2019.04.014.

Přehled o publikaci