Measuring and prognosis of remaining useful life of light-emitting diodes based on nonlinear fuzzy inference system

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F26%3A0200675" target="_blank" >RIV/00216305:26210/26:0200675 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1016/j.measurement.2026.120322" target="_blank" >https://doi.org/10.1016/j.measurement.2026.120322</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.measurement.2026.120322" target="_blank" >10.1016/j.measurement.2026.120322</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Measuring and prognosis of remaining useful life of light-emitting diodes based on nonlinear fuzzy inference system

Popis výsledku v původním jazyce

Measuring and predicting the remaining useful life (RUL) of products and engineering systems is crucial for effective health monitoring and maintenance planning. The key challenges in RUL prediction lie in acquiring relevant health indicators and constructing accurate predictive models based on these indicators. However, direct health indicator data that reflect product degradation are not always accessible; in some cases, only indirect informative measurements are available. This article addresses such a scenario with light-emitting diodes (LEDs). The article focuses on finding a feasible approach to RUL prediction using a non-linear fuzzy inference system (FIS). We introduce an optimization/training framework that integrates Bayesian optimization, multiobjective genetic algorithms, regression techniques, and F-Test feature selection to estimate the model's structural and operational parameters effectively. Online RUL prediction and parameter adaptation are achieved through the approaches based on the particle filter (PF), Huber likelihood, and recursive least squares (RLS) methods. The proposed methodology demonstrates promising predictive performance, enabling the prediction of RUL based on available indirect measurements.

Název v anglickém jazyce

Measuring and prognosis of remaining useful life of light-emitting diodes based on nonlinear fuzzy inference system

Popis výsledku anglicky

Measuring and predicting the remaining useful life (RUL) of products and engineering systems is crucial for effective health monitoring and maintenance planning. The key challenges in RUL prediction lie in acquiring relevant health indicators and constructing accurate predictive models based on these indicators. However, direct health indicator data that reflect product degradation are not always accessible; in some cases, only indirect informative measurements are available. This article addresses such a scenario with light-emitting diodes (LEDs). The article focuses on finding a feasible approach to RUL prediction using a non-linear fuzzy inference system (FIS). We introduce an optimization/training framework that integrates Bayesian optimization, multiobjective genetic algorithms, regression techniques, and F-Test feature selection to estimate the model's structural and operational parameters effectively. Online RUL prediction and parameter adaptation are achieved through the approaches based on the particle filter (PF), Huber likelihood, and recursive least squares (RLS) methods. The proposed methodology demonstrates promising predictive performance, enabling the prediction of RUL based on available indirect measurements.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

21100 - Other engineering and technologies

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2026

Kód důvěrnosti údajů

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

Název periodika

MEASUREMENT

ISSN

0263-2241

e-ISSN

1873-412X

Svazek periodika

264

Číslo periodika v rámci svazku

January 2026

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

23

Strana od-do

333-355

Kód UT WoS článku

001662877900001

EID výsledku v databázi Scopus

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