A method for extracting performance metrics using work-order data
Bibliographic record
Abstract
Holistic performance metrics are necessary to understand how operational resources are used and to detect anomalous zones, floors, equipment, and work-order categories in large commercial and institutional buildings. Work-order data in computerized maintenance management systems (CMMS) represent an untapped potential to extract such performance metrics. In this paper, a method to conduct text analytics on CMMS data is developed and demonstrated through a case study in which four years’ worth of data from four large commercial buildings are used. Association rule mining technique is employed to identify building, system, and component-level recurring work-order taxonomies and common failure modes. The results highlight the potential of kernel density functions, decision trees, Sankey diagrams, survival curves and stacked line plots to effectively visualize the temporal, spatial, and categorical anomalies in the complaint patterns. It is identified that often only a few floors and complaint types account for most of the complaints in a building. The analysis of operator comments reveal that the most frequent lighting-related complaints are resolved by replacing ballasts and lights, and the thermal and air quality complaints are addressed by adjusting the temperature setpoints, airflow rates, and fan operation schedules.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".