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Record W4220842891 · doi:10.1080/23744731.2022.2058262

Classification of sequencing logic faults in multiple zone air handling units: A review and case study

2022· review· en· W4220842891 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

VenueScience and Technology for the Built Environment · 2022
Typereview
Languageen
FieldEnergy
TopicEnergy Efficiency and Management
Canadian institutionsNational Research Council CanadaCarleton University
Fundersnot available
KeywordsSetpointVariable air volumeFan coil unitEngineeringControl logicComputer scienceReliability engineeringControl engineeringAir conditioningComputer hardwareArtificial intelligenceMechanical engineering

Abstract

fetched live from OpenAlex

This paper classifies common sequencing logic faults in multiple zone variable air volume (VAV) air handling unit (AHU) systems by conducting a critical review of the literature. Six broad categories of sequencing logic faults are identified affecting the state of operation, mode of operation, and supply air temperature and duct static pressure setpoint reset programs. A case study with building automation system (BAS) trend data from two VAV AHU systems is conducted to provide examples of these sequencing logic faults. Four of the six fault types are found present in at least one of the two AHUs. The most significant fault discovered was an incorrect reference to a nonexistent VAV terminal damper address forcing an unnecessarily high duct static pressure setpoint and wasting fan electricity. The BAS programs are reviewed in consultation with the operations staff, and the coding mistakes leading to the detected anomalous behavior are identified.

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)0.002
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.991
Threshold uncertainty score0.618

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.108
GPT teacher head0.317
Teacher spread0.209 · 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