Deployment of real-time building automation system-integrated inverse-model-based fault detection and diagnostics algorithms
Bibliographic record
Abstract
The complex operation of HVAC systems in large commercial buildings warrants regular implementation of advanced and various analytical approaches to operations and maintenance, and subsequent corrective measures to improve and maintain optimal energy performance. Despite the established capabilities of data-driven fault detection and diagnostics (FDD) to identify suboptimal controls policies and mechanical faults resulting in poor energy performance, and the growing adoption to archive data from building automation systems (BAS), few attempts have been made to disseminate and deploy scalable solutions around these approaches. Furthermore, real-time FDD approaches traditionally applied within BAS are predominantly rule-based, offering limited insights to operating deficiencies with gradual negative impacts to energy performance. This paper presents a scalable prototype of a web-based application employing various established data-driven, inverse model-based FDD methodologies in a real-time, BAS-integrated environment. This novel implementation facilitates automatic and recurring analyses of HVAC controls network data and can provide on-demand actionable visualizations and key performance indicators (KPI) to building operators. Since October 14th, 2022, the application was deployed in an office building in Ottawa, Canada. The application successfully identified a mode of operation anomaly on two separate occasions, and its outputs were presented to the corresponding building operators who promptly resolved the issues.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".