Drilling head knives degradation modelling based on stochastic diffusion processes backed up by state space models

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60162694%3AG43__%2F22%3A00557234" target="_blank" >RIV/60162694:G43__/22:00557234 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/62156489:43110/22:43920284

Výsledek na webu

<a href="https://authors.elsevier.com/sd/article/S0888-3270(21)00795-0" target="_blank" >https://authors.elsevier.com/sd/article/S0888-3270(21)00795-0</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.ymssp.2021.108448" target="_blank" >10.1016/j.ymssp.2021.108448</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Drilling head knives degradation modelling based on stochastic diffusion processes backed up by state space models

Popis výsledku v původním jazyce

In this article, we investigate a mining system consisting of dataset records on in-field operational characteristics of a drilling head. We work with these data in order to get a picture of system degradation and actual condition. For data assessment and modelling, we apply both improved and specific new mathematical models. We examine the data using extended and enhanced state space models, which are suitable for system state and condition investigation. Our time series approaches are based on a modified Kalman-type backpropagation recursion. The improved and modified state space models are accompanied by improved forms of selected stochastic diffusion processes. The diffusion processes are used both for degradation modelling and also for forecasting potential failure occurrence.

Název v anglickém jazyce

Drilling head knives degradation modelling based on stochastic diffusion processes backed up by state space models

Popis výsledku anglicky

In this article, we investigate a mining system consisting of dataset records on in-field operational characteristics of a drilling head. We work with these data in order to get a picture of system degradation and actual condition. For data assessment and modelling, we apply both improved and specific new mathematical models. We examine the data using extended and enhanced state space models, which are suitable for system state and condition investigation. Our time series approaches are based on a modified Kalman-type backpropagation recursion. The improved and modified state space models are accompanied by improved forms of selected stochastic diffusion processes. The diffusion processes are used both for degradation modelling and also for forecasting potential failure occurrence.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20301 - Mechanical engineering

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název periodika

Mechanical Systems and Signal Processing

ISSN

0888-3270

e-ISSN

—

Svazek periodika

166

Číslo periodika v rámci svazku

108448

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

21

Strana od-do

nestrankovano

Kód UT WoS článku

000704880300001

EID výsledku v databázi Scopus

2-s2.0-85115385379