LED Reliability Assessment Using a Novel Monte Carlo-Based Algorithm
Bibliographic record
Abstract
Application of Monte Carlo (MC) simulations in the statistical analysis of LED lumen maintenance is presented in this paper. Lumen maintenance data is acquired using experimental tests accomplished in the electro-optics laboratory of the Mazinoor lighting industry, which is an accredited laboratory by Iranian National Standards organization. The sampling rate and the duration of the experiments are consistent with LM-80-15 standard introduced by the Illumination Engineering Society of North America. In some cases, due to the existence of nonlinear dynamics in real trends of light flux, particularly in the first 1,000 hours, features are not completely captured using traditional reliability assessment techniques such as TM-21. In this study, a two-phase model is applied to cover features in lumen maintenance data. Furthermore, to estimate the parameters of the dedicated model in mild and severe operating conditions, a nonlinear Kalman filter-based method known as the iterated extended Kalman filter (IEKF) is used. A set of MC simulations are run to construct the probability density functions (PDFs) for the estimated parameters. Each simulation uses different values of the parameters chosen from the corresponding distribution. Finally, lifetime PDFs are constructed to extract reliability indices. All of the simulations are conducted in MATLAB and the results are compared with the conventional and well-known TM-21 approach.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".