Investigation of Field Failures of Power Systems: A Different Whisker Story
Bibliographic record
Abstract
ABSTRACT Several years ago a client experienced higher than expected failures of power controllers for large disk drive systems. The visual failure mode was severe heating of high power switching transistors, sufficient to cause partial melting of the leads and burning of the package and printed circuit board surfaces. The initial challenge was to find physical evidence of the cause starting with the failure location. Through the investigation which lasted several months, we found no evidence for obvious suspect causes, such as internal component and PCB failure, or flux residues that could cause short-circuit by ionic migration and dendrites. The first significant clue was finding a short-circuited low power control transistor, caused by a metallic filament bridging the leads. The filament was removed and SEM analysis revealed it to be a zinc whisker. Since there were no sources of zinc in the component packages or circuit assembly, we started to look for an external source of zinc whiskers. The investigation led back to the customer data centers and this paper will describe the discovery process and findings, and the corrective actions implemented to reduce the likelihood of future failures.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".