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Record W2811221022 · doi:10.4050/f-0074-2018-12849

Estimating On-condition Direct Maintenance Cost (DMC)

2018· article· en· W2811221022 on OpenAlexaff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicReliability and Maintenance Optimization
Canadian institutionsBell (Canada)
Fundersnot available
KeywordsReliability engineeringComputer scienceEnvironmental scienceProcess engineeringEngineering

Abstract

fetched live from OpenAlex

Aftermarket support has become a key component of cost and competitiveness in the rotorcraft industry. Both the operator and the rotorcraft manufacturer play a role in aftermarket support. Many rotorcraft original equipment manufacturers (OEMs) are offering fixed price maintenance service programs in their after-market support programs. Since product support may last well over two decades, the desire for low direct operating cost (DOC) and lower life cycle cost (LCC) has become a more visible consideration in the rotorcraft design phase. Direct maintenance cost (DMC), forms a significant part of the DOC and LCC. A subset of DMC, On-condition maintenance cost is a category with unspecified maintenance intervals and presents one of the more challenging estimating efforts, particularly on a new rotorcraft program with no history. The approach used for estimating maintenance costs can strongly influence decision making within the OEM while also educating the customer on better maintenance philosophy and planning. Incorrectly minimizing or excluding the effect of on-condition cost (within the DMC estimate) could have a profound impact on operator and service organizations of the OEM. This paper presents a high-level discussion on the potential refinements that can be made to the Helicopter Association International’s Economic Committee’s Guide for the Presentation of Helicopter Operating Cost Estimates 2010. Estimating the on-condition direct maintenance cost for airframe manufacturers is the focus of the discussion.

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.

How this classification was reachedexpand

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 score0.948
Threshold uncertainty score0.519

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 head0.008
GPT teacher head0.227
Teacher spread0.219 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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