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 and Jan KOLÁČEK

Basic information

Original name

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

Authors

VALIŠ, David (203 Czech Republic); Ondřej POKORA (203 Czech Republic, guarantor, belonging to the institution) and Jan KOLÁČEK (203 Czech Republic, belonging to the institution)

Edition

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

Other information

Language

English

Type of outcome

Article in a journal

Field of Study

50902 Social sciences, interdisciplinary

Country of publisher

United Kingdom of Great Britain and Northern Ireland

Confidentiality degree

is not subject to a state or trade secret

References:

Full Text

Impact factor

Impact factor: 2.897

RIV identification code

RIV/00216224:14310/19:00107351

Organization unit

Faculty of Science

DOI

https://doi.org/10.1016/j.engfailanal.2019.04.014

UT WoS

000464960500037

EID Scopus

2-s2.0-85064196405

Keywords in English

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

Abstract

In the original language

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.

Links

GJ17-22950Y, research and development project

Name: Statistická inference pro složité náhodné procesy v ekonometrickém modelování

Investor: Czech Science Foundation

Cite

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

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