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Record W2578404781 · doi:10.1155/2017/1763934

Intelligent Vehicle Embedded Sensors Fault Detection and Isolation Using Analytical Redundancy and Nonlinear Transformations

2017· article· en· W2578404781 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Control Science and Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicFault Detection and Control Systems
Canadian institutionsUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of CanadaAgence Nationale de la Recherche
KeywordsRedundancy (engineering)Fault detection and isolationResidualNonlinear systemEngineeringComputer scienceIsolation (microbiology)Control engineeringTransformation (genetics)Fault (geology)Control theory (sociology)Reliability engineeringAlgorithmArtificial intelligence

Abstract

fetched live from OpenAlex

This work proposes a fault detection architecture for vehicle embedded sensors, allowing to deal with both system nonlinearity and environmental disturbances and degradations. The proposed method uses analytical redundancy and a nonlinear transformation to generate the residual value allowing the fault detection. A strategy dedicated to the optimization of the detection parameters choice is also 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)0.001
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: Empirical
Teacher disagreement score0.711
Threshold uncertainty score0.323

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 communication0.0000.001
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 head0.011
GPT teacher head0.247
Teacher spread0.236 · 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