A proactive fault detection and diagnostics method for zone-level auto-commissioning in VAV AHUs
Bibliographic record
Abstract
Fault detection and diagnostics (FDD) in HVAC systems is critical for maintaining optimal indoor environmental quality and ensuring energy efficiency. Undetected faults in HVAC components can lead to increased energy consumption, occupant discomfort, and higher maintenance costs. Traditional FDD requires symptoms to appear during the normal operation of a building, which may require waiting for a considerable amount of time until enough evidence is naturally collected for the correct diagnosis. To address this challenge, the paper presents an automated commissioning approach for detecting zone-level faults in variable air volume (VAV) systems served by an air handling unit (AHU). The proposed method consists of four on-demand automated tests designed to isolate specific faults, including airflow faults, heating faults, and cooling and heating mode control errors. Each test involves an actuation step, where a setpoint value is adjusted or a data point is overwritten in the building automation system (BAS), followed by a waiting period and a fault condition check. The method was applied to an academic office building in Ottawa, Canada, where it successfully identified five naturally occurring zone-level faults across different test categories in a total of 11 zones. These findings demonstrate the potential of this automated commissioning approach for early fault detection in HVAC systems. As a next step, this method will be expanded to detect system-level faults, while future work should also explore the development of automated fault correction sequences.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".