Electronic Prognostics - A Case Study Using Switched-Mode Power Supplies (SMPS)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it