Bearing fault prognostics using Rényi entropy based features and Gaussian process models

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23220%2F15%3A43925112" target="_blank" >RIV/49777513:23220/15:43925112 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1016/j.ymssp.2014.07.011" target="_blank" >http://dx.doi.org/10.1016/j.ymssp.2014.07.011</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.ymssp.2014.07.011" target="_blank" >10.1016/j.ymssp.2014.07.011</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Bearing fault prognostics using Rényi entropy based features and Gaussian process models

Popis výsledku v původním jazyce

Bearings are considered to be the most frequent cause for failures in rotational machinery. Hence efficient means to anticipate the remaining useful life (RUL) on-line, by processing the available sensory records, is of substantial practical relevance. Many of the data-driven approaches rely on conjecture that evolution of condition monitoring (CM) indices is related with the aggravation of the condition and, indirectly, with the remaining useful life of a bearing. Problems with trending may be threefold: (i) most of the operational life show no significant trend until the time very close to failure; this is usually accompanied by rapidly growing values of CM indices which is not easy to forecast, (ii) the evolution of CM indices is not necessarily monotonous, (iii) variable and immeasurable fluctuations in operating may fool the trend. Motivated by these issues we propose an approach for bearing fault prognostics that employs Rényi entropy based features. It exploits the idea that pro

Název v anglickém jazyce

Bearing fault prognostics using Rényi entropy based features and Gaussian process models

Popis výsledku anglicky

Bearings are considered to be the most frequent cause for failures in rotational machinery. Hence efficient means to anticipate the remaining useful life (RUL) on-line, by processing the available sensory records, is of substantial practical relevance. Many of the data-driven approaches rely on conjecture that evolution of condition monitoring (CM) indices is related with the aggravation of the condition and, indirectly, with the remaining useful life of a bearing. Problems with trending may be threefold: (i) most of the operational life show no significant trend until the time very close to failure; this is usually accompanied by rapidly growing values of CM indices which is not easy to forecast, (ii) the evolution of CM indices is not necessarily monotonous, (iii) variable and immeasurable fluctuations in operating may fool the trend. Motivated by these issues we propose an approach for bearing fault prognostics that employs Rényi entropy based features. It exploits the idea that pro

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

JA - Elektronika a optoelektronika, elektrotechnika

OECD FORD obor

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Projekt

<a href="/cs/project/EE2.3.30.0013" target="_blank" >EE2.3.30.0013: Excelence lidských zdrojů jako zdroj konkurenceschopnosti</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2015

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

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Svazek periodika

2015

Číslo periodika v rámci svazku

52-53

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

11

Strana od-do

327-337

Kód UT WoS článku

000345472500022

EID výsledku v databázi Scopus

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