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Ground Fault Diagnostics for Automotive Electronic Control Units

2023· article· en· W4379365342 on OpenAlex
Xinyu Du, Shengbing Jiang, Dongyi Zhou, Alaeddin Bani Milhim, H. Mohseni Sadjadi

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

VenueInternational Journal of Prognostics and Health Management · 2023
Typearticle
Languageen
FieldEngineering
TopicReal-time simulation and control systems
Canadian institutionsGeneral Motors (Canada)
Fundersnot available
KeywordsCAN busOffset (computer science)GroundVoltageFrame (networking)Automotive industryComputer scienceElectronic control unitEngineeringReal-time computingEmbedded systemAutomotive engineeringElectrical engineeringComputer hardwareComputer network

Abstract

fetched live from OpenAlex

An electronic control unit (ECU) with a floating ground is not able to receive or transmit messages or participate in controller area network (CAN) communication. The absence of any ECU, either temporarily or permanently, negatively impacts vehicle functionalities. The offset ground, which by itself will not affect bus functionalities if the grounding resistance is small, however, may evolve into a floating ground or behave similarly if the resistance is large. In this work, the correlation among ground faults, either offset or floating, and CAN bus voltage or messages are analyzed based on the equivalent circuit models and the bus protocol. A voltage-based solution to detect ground faults is proposed. With the help of bus messages, both faults can be isolated at the ECU level. Considering the inherent system delay between the message fetching and voltage measurement, a normalized voltage-message correlation approach with the bus load estimation is developed as well. All proposed approaches are implemented to an Arduino-based embedded system and validated on a vehicle frame.

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: none
Teacher disagreement score0.862
Threshold uncertainty score0.369

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 communication0.0000.000
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 head0.026
GPT teacher head0.304
Teacher spread0.279 · 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