A hybrid clustering approach integrating first-principles knowledge with data for fault detection in HVAC systems
Bibliographic record
Abstract
The building sector, primarily through heating, ventilation, and air conditioning (HVAC) systems, accounts for 30% of global final energy consumption and 26% of energy-related emissions, highlighting the urgency for efficient energy management and effective fault detection. Optimizing HVAC system performance is crucial for energy conservation and sustainability. This study introduces a hybrid modeling methodology to enhance HVAC systems’ fault detection and isolation (FDI). Using feature extraction through principal component analysis (PCA) and autoencoder (AE), the proposed approach integrates first-principles knowledge with data to improve the performance of different clustering algorithms (K-means, density-based spatial clustering of applications with noise (DBSCAN), and ordering points to identify the clustering structure (OPTICS) to distinguish datasets of different operating conditions (normal and faulty conditions). The proposed approach is applied to detect common faults in HVAC systems, demonstrating superior performance compared to purely data-driven methods.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".