Markov Chain Modeling for Reliability Analysis of Multi-Phase Buck Converters
Bibliographic record
Abstract
In recent years, the structure of multi-phase buck converter also called Interleaved Buck Converter (IBC) has gained considerable attention. The advantages of the IBC in comparison to the conventional Buck converter (CBC) are the lower output current ripple, higher efficiency, fast transient response, lower electromagnetic interference and higher reliability. Since more than one stage is employed in the IBC, this converter is highly reliable. In this paper, the reliability and mean time to failure (MTTF) of the CBC, and two- and three-stage IBCs are figured out. Using the obtained results and considering various scenarios, a comprehensive comparison is provided. In addition, the operation of the converter in case of fault occurrence for high and low capacities is analyzed and reliability is evaluated in each state. The relation between the reliability and temperature of semiconductor elements is discussed. Furthermore, a laboratory-scaled prototype is used to extract the experimental results of the temperature variation of the elements during a fault. Markov model is used to evaluate the analyzed reliability.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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 | 0.000 |
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 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".