A Study on Augmented Reality Remote Maintenance Support System for Ships and Offshore Structures
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».