A Hybrid-based Clustering Approach for Fault Detection in HVAC Systems
Bibliographic record
Abstract
This paper presents a hybrid model-based fault detection strategy for heating, ventilation, and air conditioning (HVAC) systems, focusing on air handling units (AHUs). Addressing the substantial energy inefficiencies in commercial buildings due to undetected HVAC faults, this research combines first-principles knowledge with data-driven techniques to enhance fault detection accuracy. First-principles based residuals (differences between expected and observed behaviors) are integrated with data (temperature measurements in different locations of AHU) to perform principal component analysis (PCA) (pre-processing step). Pre-processed data (principal component scores) are then utilized to perform clustering analysis using K-means and DBSCAN approaches. The proposed approach is tested against two common faults in AHUs and its performance is evaluated compared to a purely data-driven method. The results indicate that the hybrid method, which synergizes residual knowledge from first-principles models with data, significantly outperforms the purely data-driven approach. This is demonstrated through performance analysis using metrics like the adjusted rand index (ARI) and normalized mutual information (NMI). The research underscores the potential of the hybrid method in improving fault diagnosis of HVAC systems, helping to conserve energy by ensuring efficient and reliable operation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".