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Record W2315225505 · doi:10.1109/tpwrd.2014.2321409

A Novel Approach to Investigate the Effect of Maintenance on the Replacement Time for Transformers

2014· article· en· W2315225505 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

VenueIEEE Transactions on Power Delivery · 2014
Typearticle
Languageen
FieldEngineering
TopicPower System Reliability and Maintenance
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsReliability engineeringOptimal maintenanceTransformerMaintenance engineeringPredictive maintenancePreventive maintenanceSchedulePlanned maintenanceEngineeringProactive maintenanceReliability (semiconductor)Condition-based maintenanceComputer sciencePower (physics)VoltageElectrical engineering

Abstract

fetched live from OpenAlex

An approach to link maintenance and replacement decisions is presented in this paper. This approach proposes a methodical decision-making system to determine the optimal time to replace equipment. It essentially investigates the cost-effectiveness of replacing the equipment both before and after the lifetime is extended by maintenance. To properly investigate the effect of maintenance, maintenance activities should first be scheduled effectively. Therefore, this approach introduces a maintenance strategy based on reliability-centered maintenance (RCM) concept and genetic algorithm (GA) to optimally schedule maintenance activities. Two replacement studies are conducted: with and without the effect of maintenance. A comparison between replacement studies is discussed in the proposed approach. The proposed approach is applied to one of the most critical pieces of equipment in power systems: power transformer.

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)0.001
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: none
Teacher disagreement score0.933
Threshold uncertainty score0.521

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.187
Teacher spread0.180 · 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