Technologies for Fire and Damage Control and Condition Based Maintenance
Bibliographic record
Abstract
Abstract : This is the final report of Applied Research Program (ARP) project 11gy Technologies for Fire and Damage Control and Condition Based Maintenance. The project objective was to develop an improved understanding of how materials, sensors and sensor systems choices impact the sustainability and supportability of new build ships from both the damage control and condition based maintenance perspectives. Specifications, standards and methods for the evaluation of the fire performance of non-metallic materials are reviewed. Although no one method can be used to rank materials, Cone calorimetry is the test method that provides the most useful information on how materials might perform in a fire. A volume sensor system (VSS), named the Canadian Demonstrator Prototype (CDP), was purchased and evaluated on the United States Naval Research Laboratory fire research ship the ex-USS Shadwell. A volume sensor system monitors a space for fire and damage events using video and infrared cameras, infrared and ultraviolet spectral sensors and an acoustic sensor. The system also has data fusion software that analyses the sensor input and determines if the input is consistent with a fire or damage event or is the result of shipboard activities that are not related to fire and damage events. The results of the testing indicated that the system could differentiate between real fire and damage scenarios and shipboard activities and events that are not related to fire and damage events and could therefore reduce false alarms. A condition based monitoring (CBM) diesel engine lubricating oil sensor suite and system was developed and trialled on an operational Canadian Patrol Frigate. The goal of this program is to base maintenance on the condition of the engine and its oil as opposed to performing time based maintenance. This will enable ship?s crews to focus maintenance efforts on engines where it is required and eliminate maintenance when it is not required. The effecti
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".