Operational failure assessment of Remotely Operated Vehicle (ROV) in harsh offshore environments
Bibliographic record
Abstract
For an effective integrity assessment of marine robotic in offshore environments, the elements’ failure characteristics need to be understood. A structured probabilistic methodology is proposed for the operational failure assessment (OFA) characteristics of ROV. The first step is to assess the likely failure mode of the ROV system and its support systems. This captures the interaction and failure induced events during operation. The identified potential failure modes are further developed into logical connectivity based on the cause-effect relationship. The logical framework is modeled using the fault tree analysis technique to predict the ROV operational failure probability in an uncertain harsh environment. The fault tree analysis captured the logical relationship between the primary, intermediate, and top events probability. The importance measure criteria were adopted to identify the most probable events, links, and their importance on the failure propagation. The model was demonstrated with an ROV for deep arctic water subsea operations. The result identified the control system, communication linkages, human factor, among others, as most critical in the ROV operational failure. The methodology’s application provides core information on the Mean time between failure (MTBF) of the ROV system that could aid integrity management and provides a guide on early remedial action against total failure.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".