Degradation process and failure estimation of drilling system based on real data and diffusion process supported by state space models

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43110%2F20%3A43918053" target="_blank" >RIV/62156489:43110/20:43918053 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/60162694:G43__/20:00555846

Výsledek na webu

<a href="https://doi.org/10.1016/j.measurement.2020.108076" target="_blank" >https://doi.org/10.1016/j.measurement.2020.108076</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.measurement.2020.108076" target="_blank" >10.1016/j.measurement.2020.108076</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Degradation process and failure estimation of drilling system based on real data and diffusion process supported by state space models

Popis výsledku v původním jazyce

Technical systems used in adverse environments are subject to very intense degradation and their parts deterioration. Due to the problematic placement of some parts, it is sometimes very difficult, to indicate the level of degradation and possible failure occurrence. Therefore, it is very useful to work with the available field operation data. Since we possess such data and apply progressive methods to model the degradation, we are able to predict the possible failure occurrence and forecast residual useful life. At first, we apply spectral analysis approaches. The spectral analysis is used to capture extreme values in the data structure. The extreme values are later filtered out to avoid future estimations which might be affected by the deformed inputs. In the next step, we use non-parametric smoothing and state space models to acquire trend, variance and related statistics in the data structure. These characteristics are later used as input parameters for specific and new forms of diffusion processes. With these diffusion processes we would like to model the degradation evolvement and failure occurrence. The failure occurrence is represented as one of the statistics of the first passage time (FPT). FPT is a moment when the modelled trajectory hits the predefined threshold - such threshold represents a critical limit for our observation. The outcomes are useful for (i) degradation modelling, deterioration prediction and condition assessment, (ii) operation and maintenance planning and rationalisation, and (iii) life cycle cost optimisation and safety improvement.

Název v anglickém jazyce

Degradation process and failure estimation of drilling system based on real data and diffusion process supported by state space models

Popis výsledku anglicky

Technical systems used in adverse environments are subject to very intense degradation and their parts deterioration. Due to the problematic placement of some parts, it is sometimes very difficult, to indicate the level of degradation and possible failure occurrence. Therefore, it is very useful to work with the available field operation data. Since we possess such data and apply progressive methods to model the degradation, we are able to predict the possible failure occurrence and forecast residual useful life. At first, we apply spectral analysis approaches. The spectral analysis is used to capture extreme values in the data structure. The extreme values are later filtered out to avoid future estimations which might be affected by the deformed inputs. In the next step, we use non-parametric smoothing and state space models to acquire trend, variance and related statistics in the data structure. These characteristics are later used as input parameters for specific and new forms of diffusion processes. With these diffusion processes we would like to model the degradation evolvement and failure occurrence. The failure occurrence is represented as one of the statistics of the first passage time (FPT). FPT is a moment when the modelled trajectory hits the predefined threshold - such threshold represents a critical limit for our observation. The outcomes are useful for (i) degradation modelling, deterioration prediction and condition assessment, (ii) operation and maintenance planning and rationalisation, and (iii) life cycle cost optimisation and safety improvement.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

50202 - Applied Economics, Econometrics

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Measurement

ISSN

0263-2241

e-ISSN

—

Svazek periodika

164

Číslo periodika v rámci svazku

November

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

11

Strana od-do

108076

Kód UT WoS článku

000548651300015

EID výsledku v databázi Scopus

2-s2.0-85086572251