Fault Detection and Diagnosis for Central Heating System Using Equipment Emulators and Vibration Monitoring Techniques
Bibliographic record
Abstract
The presented research provides a novel approach for the development of Fault Detection and Diagnosis (FDD) algorithms for equipment commonly found within legacy buildings. A physics based non-condensing boiler model – capable of emulating 3 types of faults – was designed as the basis for the boiler FDD. The model outputs were then classified using machine learning algorithms. The pump FDD was performed using time-series analysis of experimental vibration data of common bearing faults as inputs to machine learning algorithms. Both boiler and pump models were highly accurate, averaging a classification accuracy of 94% and 99%, respectively. This research provides operators with the ability to leverage Building Automation Systems (BAS) and vibration data to detect and track equipment degradation or poor operating characteristics. The FDD tool developed in this research enables HVAC equipment issues to be rapidly corrected and maintain a high level of efficiency – maximizing service life while reducing cost and energy use.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".