Bridging the gap between theory and adoption: A critical review of socio-technical and human-computer interaction studies of fault detection and diagnosis in commercial buildings
Bibliographic record
Abstract
Over the past two decades, extensive research has covered various automated fault detection and diagnostic (AFDD) methods. Nonetheless, there are only limited examples where these tools’ usability and adoption are investigated. To address this gap, this review paper investigates two main topics that are relevant to AFDD adoption: (1) the socio-technical challenges faced by facility management (FM) organizations that are the primary target of AFDD tools and (2) user testing and human-computer interaction (HCI) based studies of AFDD and other energy information and management technology. We argue that along with the extensive research on AFDD strategies, these two topics are essential to address the challenges of AFDD adoption and to shape the direction of future AFDD research. The available literature suggests a gap in understanding what design elements of novel AFDD tools and techniques lead to industry use and, ultimately, fault correction. Without further advancements toward understanding the practical requirements for AFDD adoption, this gap leaves researchers and the industry with limited knowledge to improve the design of future AFDD tools. To bridge the gap between theory and adoption, we recommend the expanded use of HCI methods in AFDD development to address the socio-technical challenges faced by FM organizations.
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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 | 0.012 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".