Advancing Predictive Maintenance: Median-Based Particle Filtering in MOSFET Prognostics
Bibliographic record
Abstract
The burgeoning demand for effective predictive maintenance in industrial systems necessitates accurate prognostics of equipment's Remaining Useful Life (RUL).In particular, the prediction of RUL plays a crucial role in MOSFET devices for many applications to prevent failures and maintenance schedule optimization.Motivated by the potential requirement for more than what traditional models can provide, this study sought to improve upon these estimates.This research aimed to solve the problem of accurately estimating RUL for MOSFET devices, since these systems are characterized by substantial uncertainties and non-linear processes.To address this issue, we introduced a highperformance prognostic model that promises to take advantage of both the Adaptive Particle Filter (APF) and Gaussian Process Regression (GPR), which is called as PF-GPR method.The model used a genetic algorithm to control when and how many particles were adaptively resampled applying different weighted strategies (mean or median) in response of the stochastic system deterioration.We designed and conducted a series of experiments based on the PF-GPR approach with two objectives: i) to benchmark different systematic resampling schemes; ii) demonstrate that even in case of small number (i.e.FloatTensor section {newString 10}0/75k).Systematic comparisons with standard particle filtering techniques showed the model performed well in tracking RUL as it degraded through wear degrees and estimated prediction errors were obtained.The results showed that the PF-GPR model significantly outperformed traditional methods, in particular their adpative resampling version based on the median.The PF-GPR median approach performed consistently better with respect to true RUL approximation and had the lowest RMSE for all time points.These results highlight the improved strength of prediction power for equipment RULs using our provided approach, thus reinforcing that real-world predictive maintenance applications are possible by said model.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".