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@article{1520497, author = {Vališ, David and Pokora, Ondřej and Koláček, Jan}, article_location = {Oxford}, article_number = {JUL 2019}, doi = {http://dx.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://dx.doi.org/10.1016/j.engfailanal.2019.04.014.
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