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Record W1641855109 · doi:10.1109/iecon.1996.571027

An inspection strategy for randomly failing systems subjected to random shocks

2002· article· en· W1641855109 on OpenAlexaff
Anis Chelbi, Daoud Aı̈t-Kadi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicReliability and Maintenance Optimization
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsReliability engineeringComputer scienceCensoring (clinical trials)Electric power systemProcess (computing)ALARMTransient (computer programming)Critical systemStochastic processAutomotive industryReal-time computingEngineeringPower (physics)MathematicsStatisticsElectrical engineering

Abstract

fetched live from OpenAlex

This paper is motivated by the study of randomly failing systems whose state is only known through inspection and for which high availability is required. Security and alarm systems such as power system protective relays and environmental censoring equipment are typical examples of such systems. The deterioration process of these systems is generally governed by electromechanical transient shocks which occur randomly over time and whose magnitude is also random. These shocks damage the system cumulatively. Under the proposed inspection strategy, the system is inspected at predetermined times T/sub 1/, T/sub 2/, ... . If failure is detected then the system is replaced by a new one, otherwise it is kept operating. The expression of the system time-stationary availability is presented and an algorithm has been developed to generate the inspection sequence which insures a certain availability level. In cases where limited resources restrict the user to predetermined inspection period, the computer program generates the optimal design and operating parameters which will provide the targeted system availability level. A test case is analyzed and potential applications related to automotive industries are mentioned.

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 score0.871
Threshold uncertainty score0.398

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 head0.017
GPT teacher head0.222
Teacher spread0.206 · 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
Published2002
Admission routes1
Has abstractyes

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