Bibliographic record
Abstract
A facility management strategy requires that an organization's major operational concerns are dealt with, such as: avoiding the risk of catastrophic failures, planning for asset maintenance and reducing the quantity of spare parts and associated inventory costs. To bring this into further perspective, it is a well known fact that many systems suffer increasing wear with usage and age and are subject to random failures that are linked to the deterioration of these assets. Some examples of such affected items can be building components, hydraulic structures, turbine blades, and rotating equipment. In these cases, various physical deterioration processes can be observed, such as cumulative wear, crack growth, corrosion, fatigue, and so on. The deterioration and failures of such systems might incur safety hazards, as well as high operational costs (due to work stoppage, delays, unplanned intervention, etc.). To cope with this, preventive maintenance strategies are often adapted thereby replacing the deteriorated system before it even fails. If the deterioration of the system, or a parameter strongly correlated with the state of that system can be directly measured (via corrosion assessment, wear monitoring, etc.), and if the system stops functioning when it deteriorates beyond a given threshold, then it is appropriate to base any maintenance decisions on the actual deterioration of the system rather than on its age. And this leads to the choice of a condition-based maintenance (CBM) policy. CBM techniques provide an assessment of the system's condition, based on data collected from the system through continuous monitoring and/or via inspections. The main intent is to determine the required maintenance plan prior to any predicted failure. Such a strategy will contribute by minimizing maintenance costs, improving operational safety and reducing the number of in-service system failures. This paper will address the merits of adapting CBM strategies in Facilities Management.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".