A Study on Augmented Reality Remote Maintenance Support System for Ships and Offshore Structures
Bibliographic record
Abstract
From the viewpoint of safety and sustainability, the demand for autonomous vessels is increasing.Due to technical and administrative limitations, achieving a fully autonomous ship is through sequential development and application, and this can be confirmed through the 4 unmanned surface ship degrees [1] established by the IMO.As an intermediate step, the main concern is the operation of the ship with minimal onboard crews, and this is a similar situation for offshore structures.In a crew-minimized environment, one crew member should be able to perform multi-discipline techniques, but it is practically impossible to establish such an environment in a short period of time.For this reason, research and development are focused on systemic support to onboard crews that can operate and maintain in a minimal crew environment.And the activities define a vessel in this operating environment as a smart vessel and approach it as a prestage of fully autonomous vessels.In case of smart ships, studies like [2] are being conducted on monitoring and detecting abnormal situations in equipment that occur during operation on ships.In addition, studies [3] are being conducted to converge condition monitoring data and the cyber physical system and apply them to ships and offshore structures.These studies are related to systems supporting the maintenance in point of the Fail Safety, and the purpose of the Fail Safety is to support the sustainable operation of ships or offshore structures.The Fail Safety system consists of two main components, those are the diagnosis of the equipment status based on the monitoring information and supporting a proper maintenance plan based on the diagnosis result.In this study, the Fail Safety system related to providing maintenance plan is developing to consider the working environment to support effective maintenance work information for workers who need to perform unfamiliar work in nonspecialized fields.In the above system, a maintenance support code related to an abnormal condition of equipment or system is generated through Condition Based Monitoring system and transmitted to a remote maintenance management system.Based on the maintenance support code, an appropriate Maintenance Work Package is generated which developed through this research.The selected Maintenance Work Package provides maintenance details by visualizing documents, drawings, or 3D model instructions with digital twin to workers.For maintenance situations that are not resolved through Maintenance Work Package, the system is designed to support maintenance through remote expert support or to share work status for requiring decision-making.In addition, to verify the system, the mock-up verification process of the test bed is introduced.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".