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A Comprehensive Review on Advanced Maintenance Strategies for Smart Railways

2019· review· en· W2986877236 on OpenAlexaff
Nastaran Enshaei, Amin Hammad, Farnoosh Naderkhani

Bibliographic record

VenueAdvances in logistics, operations, and management science book series · 2019
Typereview
Languageen
FieldEngineering
TopicMachine Fault Diagnosis Techniques
Canadian institutionsConcordia University
Fundersnot available
KeywordsSupply chainPrognosticsRisk analysis (engineering)SustainabilityProduction (economics)Preventive maintenanceEngineeringBusinessTransport engineeringReliability engineeringMarketing

Abstract

fetched live from OpenAlex

Recently, energy efficient and sustainable supply chain has attracted a great deal of attention. To achieve sustainability, it is of paramount importance to use environmentally friendly procedures in each stage of the supply chain including suppliers, production/manufacturing, and transportation. Transportation plays a critical role in supply chain since the suppliers and customers are typically distributed in large geographical areas. Nowadays, railway is considered as a primary mode of transportation. Railways are considered as complex systems that are subject to degradation and failures. To avoid failures, tremendous efforts should be invested on preventive maintenance (PM) for performing optimal maintenance actions. Proper PM actions such as condition-based maintenance (CBM) play a major role in keeping the supply chain from crumbling down and working with high efficiency. This chapter reviews promising research works in the application of state-of-the-art CBM strategies that contribute to the advancement of smart transportation system via fault diagnostics/prognostics.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.003

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 head0.036
GPT teacher head0.365
Teacher spread0.328 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations2
Published2019
Admission routes1
Has abstractyes

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