Predictive Maintenance Using Fouling Detection on Heat Exchangers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Predictive maintenance (PdM) in heating, ventilation, and air conditioning (HVAC) systems improves energy efficiency and extends equipment lifespan. However, developing deep learning (DL) models for PdM faces challenges due to limited labeled datasets, particularly for image-based fouling detection in cooling tower heat exchangers. To address this, a synthetic image dataset is introduced, simulating dust and particulate accumulation on heat exchanger grids. The synthetic fouling data approach generates daily fouling growth and blends synthetic dust into real images. Using this dataset, convolutional neural networks (CNNs), including UNet, UNet++, and CGNet, are trained for semantic segmentation to detect fouling levels. The models are evaluated using F1 scores and precision-recall curves. Results demonstrate that the synthetic dataset effectively enables CNNs to detect fouling, with UNet achieving the best balance between accuracy and efficiency. Contributions include the development of the first synthetic dataset tailored for fouling simulation and a trained CNN model that can be integrated into PdM systems. This work addresses data scarcity and advances the use of computer vision in PdM.
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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 it