Drilling head knives degradation modelling based on stochastic diffusion processes backed up by state space models
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43110%2F22%3A43920284" target="_blank" >RIV/62156489:43110/22:43920284 - isvavai.cz</a>
Nalezeny alternativní kódy
RIV/60162694:G43__/22:00557234
Výsledek na webu
<a href="https://doi.org/10.1016/j.ymssp.2021.108448" target="_blank" >https://doi.org/10.1016/j.ymssp.2021.108448</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>
Alternativní jazyky
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
System quality requirements are typically formed by consideration of reliability and safety performance. Failures caused by system weakness, degradation or fatigue may cause undesired, and potentially dangerous, consequences. For various reasons, not all processes of system degradation are easily monitored in the lifecycle of a system. Degradation evolution leads to changes in both performance and reliability characteristics. 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. All of these models are expected to help both with deterioration propagation assessment and with the indication of when the degradation of the system under investigation is predicted to reach the critical limit. Such a limit is represented by threshold performance characteristics that may lead to either soft or hard failure with related faults. The outcomes presented in this article may help with i) failure occurrence prediction, ii) residual useful life prognosis, iii) safer system operation, iv) system utilisation rationalisation and v) maintenance forecasting.
Název v anglickém jazyce
Drilling head knives degradation modelling based on stochastic diffusion processes backed up by state space models
Popis výsledku anglicky
System quality requirements are typically formed by consideration of reliability and safety performance. Failures caused by system weakness, degradation or fatigue may cause undesired, and potentially dangerous, consequences. For various reasons, not all processes of system degradation are easily monitored in the lifecycle of a system. Degradation evolution leads to changes in both performance and reliability characteristics. 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. All of these models are expected to help both with deterioration propagation assessment and with the indication of when the degradation of the system under investigation is predicted to reach the critical limit. Such a limit is represented by threshold performance characteristics that may lead to either soft or hard failure with related faults. The outcomes presented in this article may help with i) failure occurrence prediction, ii) residual useful life prognosis, iii) safer system operation, iv) system utilisation rationalisation and v) maintenance forecasting.
Klasifikace
Druh
J<sub>imp</sub> - Článek v periodiku v databázi Web of Science
CEP obor
—
OECD FORD obor
20501 - Materials engineering
Návaznosti výsledku
Projekt
—
Návaznosti
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Ostatní
Rok uplatnění
2022
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název periodika
Mechanical Systems and Signal Processing
ISSN
0888-3270
e-ISSN
1096-1216
Svazek periodika
166
Číslo periodika v rámci svazku
1 March
Stát vydavatele periodika
GB - Spojené království Velké Británie a Severního Irska
Počet stran výsledku
21
Strana od-do
108448
Kód UT WoS článku
000704880300001
EID výsledku v databázi Scopus
2-s2.0-85115385379