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

Result code in 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>
Result on the web
<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>

Result language
angličtina
Original language name
Measuring and prognosis of remaining useful life of light-emitting diodes based on nonlinear fuzzy inference system
Original language description
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.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
21100 - Other engineering and technologies

Publication year
2026
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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