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Record W2023381866 · doi:10.1115/detc2007-34733

Analysis and Evaluation of Reliability of Diesel Engine Based on Maintenance Records

2007· article· en· W2023381866 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

VenueVolume 4: ASME/IEEE International Conference on Mechatronic and Embedded Systems and Applications and the 19th Reliability, Stress Analysis, and Failure Prevention Conference · 2007
Typearticle
Languageen
FieldEngineering
TopicIndustrial Technology and Control Systems
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsReliability engineeringReliability (semiconductor)Diesel engineFailure mode and effects analysisDiesel fuelComputer scienceAutomotive engineeringEngineeringPower (physics)

Abstract

fetched live from OpenAlex

Evaluating reliability and finding the critical components of a diesel engine system is a very important process in quality improvement and new product design. In this paper, a series of reliability indexes were applied in diesel engine reliability evaluation based on maintenance records. Several methods including life cycle distribution and FMEA (Failure Mode and Effects Analysis) were also presented. At the end, a diesel engine (Model 6108ZQ) was used as a case study.

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)0.003
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 score0.898
Threshold uncertainty score0.769

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.021
GPT teacher head0.282
Teacher spread0.262 · 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