Toward failure mode and effect analysis for heating, ventilation and air-conditioning
Bibliographic record
Abstract
Fault Detection, Diagnostics and Prognostics (FDD&P) is attracting a lot of attention from building operators and researchers because it can help greatly improve the performance of building operations by reducing energy consumption for heating, ventilation and air-conditioning (HVAC) while improving occupant comfort at the same time. However, FDD&P for building operations remains with many challenges due to special operation environments of HVAC systems. These challenges include `tolerance or ignorance' of failures in long-haul operations, lack of operation regulations, and even lack of documents for HVAC failure mode and effect analysis (FMEA), which is a systematic method of identifying and preventing system, product and process problems. To address some of these challenges, we propose to develop a FMEA for HVAC by exploring work orders generated by building energy management systems (BEMS) using a data mining approach. With the developed HVAC FMEA, it is possible to conduct pre-FDD&P procedures to improve HVAC maintenance and to select the high impact failures in order to acquire the operation data for selected failures and develop machine learning-based predictive models to predict a failure before it occurs and isolate the root component of a given failure. In this paper we report some preliminary results in developing an HVAC FMEA tool from a large number of work orders obtained from a BEMS in routine operations. The developed HVAC FMEA will be used as a guidance tool for data gathering and developing data-driven models for building HVAC FDD&P.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".