Measuring and prognosis of remaining useful life of light-emitting diodes based on nonlinear fuzzy inference system
Artykuł w czasopiśmie
MNiSW
200
Lista 2024
| Status: | |
| Autorzy: | La Quoc Tiep, Vališ David, Vintr Zdeněk, Gajewski Jakub, Žák Libor, Kohl Zdeněk |
| Dyscypliny: | |
| Aby zobaczyć szczegóły należy się zalogować. | |
| Rok wydania: | 2026 |
| Wersja dokumentu: | Drukowana | Elektroniczna |
| Język: | angielski |
| Wolumen/Tom: | 264 |
| Numer artykułu: | 120322 |
| Strony: | 1 - 23 |
| Scopus® Cytowania: | 0 |
| Bazy: | Scopus |
| Efekt badań statutowych | NIE |
| Materiał konferencyjny: | NIE |
| Publikacja OA: | NIE |
| Abstrakty: | angielski |
| 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 in- direct 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, multi- objective genetic algorithms, regression techniques, and F-Test feature selection to estimate the model’s struc- tural 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. |
