Development of a distributed building fault detection, diagnostic, and evaluation system
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
This paper introduces a distributed system for building fault detection, diagnostic, and evaluation (FDDE). The design of the distributed system aims to address computation and network limitations on a common commercial building automation system (BAS). This system also aims to be adaptable to different fault detection and fault diagnostics algorithms developed by other researchers. The fault evaluation aspect of the system provides quantitative impact metrics of the potential faults to the building operators. Probabilistic representations of faults and symptoms are used, and a continuous symptom severity value is developed to provide more granularity over the abnormal operation information. The proposed method is then tested with five fault cases simulated in EnergyPlus. Results show reduced false positive rate and enhanced fault belief when using a dynamic Bayesian network (DBN) over the conventional event-based Bayesian network (BN) used in fault diagnostics. Fault evaluation based on continuous symptom severity provides a reasonable quantitative reference for building operators to make informed decisions. This system will be further expanded with more fault detection algorithms and tested inside real buildings, and a framework will be made available for other researchers to develop upon.
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.
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