Autoencoder-Based fault detection using building automation system data
Bibliographic record
Abstract
This paper explores the application of autoencoder algorithms in Automated Fault Detection (AFD) for Heating, Ventilation, and Air Conditioning (HVAC) systems, specifically focusing on Fan Coil Units (FCUs). The study begins by reviewing the current state of Fault Detection and Diagnostics (FDD), emphasizing the limitations and the potential of unsupervised learning techniques like autoencoders and transfer learning to fill these gaps. Using data from a full-scale building case study featuring five Fan Coil Units (FCUs), the research develops and evaluates autoencoder-based AFD models that models effectively compress multivariate inputs into a reduced latent space, enabling accurate and efficient fault detection. The paper makes two novel contributions: (1) It introduces a methodology to distinguish between equipment-level and system-level faults; and (2) It demonstrates the generalizability of the approach across different types of FCUs through cross-testing and transfer learning. The results indicate that autoencoders outperform other dimensionality reduction algorithms and separate predictors in fault detection accuracy and efficiency. The paper concludes by discussing the implications of these findings for future research and practical applications in building management.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".