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Record W3203889513 · doi:10.1016/j.aej.2021.09.056

Qualitative and quantitative analysis of the reliability of NPC and ANPC power converters for aeronautical applications

2021· article· en· W3203889513 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAlexandria Engineering Journal · 2021
Typearticle
Languageen
FieldEngineering
TopicReliability and Maintenance Optimization
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsConvertersReliability engineeringReliability (semiconductor)Failure mode and effects analysisActuatorEngineeringPower (physics)HazardComputer scienceElectrical engineeringVoltage

Abstract

fetched live from OpenAlex

In more electrical aircraft, most flight control wing surface actuators are either electro-hydrostatic or electro-mechanical, whether in manual or autopilot mode. Their reliability is essential for flight safety; failure of even one actuator can have catastrophic consequences. According to ARP4761 guidelines for the development and certification of critical aircraft systems, the probability of failure of flight-control actuator components must be less than 10-9 per hour of flight. In this paper, we compare the probability of failure per hour (PFH) of conventional two-level power converters and three-level NPC and ANPC converter topologies designed in our laboratory. In accordance with ARP procedures, PFH was calculated using an approach that includes qualitative analysis based on failure mode and effect analysis (FMEA) methodology as well as quantitative analysis based on truth tables. ANPC converter met all the requirements of the functional hazard assessment in all cases of defects. Finally, we discuss factors that determine the feasibility of implementing power converters in flight control systems.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.582
Threshold uncertainty score0.278

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.269
Teacher spread0.258 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it