MétaCan
Menu
Back to cohort
Record W2786680887 · doi:10.1109/icsrs.2017.8272806

Modeling and analysis of cluster of failures in redundant systems

2017· article· en· W2786680887 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicReliability and Maintenance Optimization
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsRedundancy (engineering)Computer scienceCluster analysisEvent (particle physics)Reliability (semiconductor)Reliability engineeringCluster (spacecraft)Electric power systemProcess (computing)Data miningPower (physics)EngineeringMachine learning

Abstract

fetched live from OpenAlex

Redundancy is an important method to improve the reliability of repairable systems. In this paper, we consider clustering of failures in redundant systems due to parallel type of carryover effects, in which the event intensity of a recurrent event process is temporarily increased after event occurrences in other processes. Our main goal is to develop formal test procedures for the assessment of such effects in redundant systems with repairable components connected in parallel and subject to recurrent failures. We develop partial score tests for testing the presence of parallel carryover effects in recurrent event settings. Asymptotic properties of the test statistics are discussed analytically as well as through simulations. A simulated data set including failure times of diesel power generators operating in remote and isolated communities is analyzed to illustrate the methods developed.

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: Empirical
Teacher disagreement score0.203
Threshold uncertainty score0.132

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 head0.010
GPT teacher head0.221
Teacher spread0.211 · 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

Quick stats

Citations4
Published2017
Admission routes1
Has abstractyes

Explore more

Same topicReliability and Maintenance OptimizationFrench-language works237,207