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Record W2132050948 · doi:10.5267/j.msl.2015.5.006

Failure mode and effect analysis on safety critical components of space travel

2015· article· en· W2132050948 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueManagement Science Letters · 2015
Typearticle
Languageen
FieldEngineering
TopicTechnology Assessment and Management
Canadian institutionsnot available
Fundersnot available
KeywordsSpace (punctuation)Failure mode and effects analysisMode (computer interface)Computer scienceBusinessProcess managementOperations managementRisk analysis (engineering)Reliability engineeringEconomicsEngineeringHuman–computer interaction

Abstract

fetched live from OpenAlex

Sending men to space has never been an ordinary activity, it requires years of planning and preparation in order to have a chance of success. The payoffs of reliable and repeatable space flight are many, including both Commercial and Military opportunities. In order for reliable and repeatable space flight to become a reality, catastrophic failures need to be detected and mitigated before they occur. It can be shown that small pieces of a design which seem ordinary can create devastating impacts if not designed and tested properly. This paper will address the use of a Failure Mode, Effects, and Criticality Analysis (FMECA) with modified Risk Priority Number (RPN) and its application to safety critical design components of shuttle liftoff. An example will be presented here which specifically focuses on the Solid Rocket Boosters (SRBs) to illustrate the FMECA approach to reliable space travel.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.477
Threshold uncertainty score0.493

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.008
GPT teacher head0.248
Teacher spread0.239 · 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