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 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, few attempts have been made to deploy scalable solutions around these approaches. Furthermore, real-time BAS-integrated FDD methods are predominantly rule-based, offering limited insights to faults with gradual negative impacts to energy performance. This paper demonstrates the application of various established data-driven, inverse-model-based FDD methodologies in a BAS-integrated environment. Traditionally implemented sparingly, the novelty of recursive and automatic execution of advanced FDD methodologies, facilitated through a direct data pipeline to an existing BAS, capitalizes on the BAS’s real-time monitoring capabilities to enable continuously refreshed inverse model generation that can capture the gradual degradation of building performance, and provide up-to-date actionable visualizations and key performance indicators (KPI) to building operators. Since deployment, the application has successfully identified a scheduling fault on two separate occasions in a case study building in Ottawa, Canada, and the visualizations were presented to the building operators who resolved the issues.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".