Publication detail
Measuring and prognosis of remaining useful life of light-emitting diodes based on nonlinear fuzzy inference system
LA, Q. VALIS, D. VINTR, Z. GAJEWSKI, J. ŽÁK, L. KOHL, Z.
English title
Measuring and prognosis of remaining useful life of light-emitting diodes based on nonlinear fuzzy inference system
Type
WoS Article
Language
en
Original abstract
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.
Keywords in English
Light-emitting diode, Reliability, Remaining useful life, Non-linear fuzzy inference system, Data structural change, Rule selection, Online prediction
Released
2026-01-07
Journal
MEASUREMENT
Volume
264
Number
January 2026
Pages from–to
333–355
Pages count
23
BIBTEX
@article{BUT200675,
author="{} and {} and {} and {} and Libor {Žák} and {}",
title="Measuring and prognosis of remaining useful life of light-emitting diodes based on nonlinear fuzzy inference system",
journal="MEASUREMENT",
year="2026",
volume="264",
number="January 2026",
pages="333--355",
doi="10.1016/j.measurement.2026.120322",
issn="0263-2241",
url="https://doi.org/10.1016/j.measurement.2026.120322"
}