Electronic Prognostics - A Case Study Using Switched-Mode Power Supplies (SMPS)
Bibliographic record
Abstract
This paper describes the process, used to develop prognostics algorithms for a commercially available switched-mode power supply (SMPS) using corroborative evidence sources. The process begins with a Pareto analysis indicating the primary modes of failure. Critical components are identified using a three-tier failure mode and effects analysis (FMEA) by investigating device, circuit, and system parameters sensitive to degradation. Once acceleration factors, or sources of degradation, are known damage accumulation failure models for each critical component are derived from highly accelerated life tests (HALT). Then, healthy components are systematically degraded to varying levels of severity by performing highly accelerated stress testing (HAST). These components are used in seeded fault tests to identify system-level parameters sensitive to device damage. Features extracted from data recorded during seeded fault tests are used to derive feature-based failure models. Finally, reasoning and data fusion algorithms are applied to both models to generate corroborative remaining useful life (RUL) predictions.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".