A Deep Learning Approach for Heating and Cooling Equipment Monitoring
Bibliographic record
Abstract
Condition monitoring is an important issue in system health management, and usually the first step leading to fault detection, diagnosis, and prognosis. It is often conducted through monitoring the time series of sensors, which usually includes outliers and change points. We adopt a deep learning model LSTM for monitoring the condition of boilers and chillers in a central heating and cooling plant. A two stage approach for condition monitoring is used: condition prediction and anomaly detection. In condition prediction stage, we use a LSTM model and three regression models: LASSO, SVR, and MLP to predict the energy efficiency of boilers and chillers. The experiments show that LSTM is able to establish a robust normal behavior of multiple boilers and chillers. LSTM reaches a lower prediction error than that of other three models in six out of nine boilers and chillers. In anomaly detection stage, we detect outliers or change points using the prediction errors from the LSTM model earlier than other models. This deep learning approach is applicable for real-time condition monitoring.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".