• Journal of the Korean Society of Marine Environment & Safety
• /
• v.27 no.2
• /
• pp.363-368
• /
• 2021

Ship engine rooms have improved automation systems owing to the advancement of technology. However, there are many variables at sea, such as wind, waves, vibration, and equipment aging, which cause loosening, cutting, and leakage, which are not measured by automated systems. There are cases in which only one engineer is available for patrolling. This entails many risk factors in the engine room, where rotating equipment is operating at high temperature and high pressure. When the engineer patrols, he uses his five senses, with particular high dependence on vision. We hereby present a preliminary study to implement an engine-room patrol robot that detects and informs the machine room while a robot patrols the engine room. Images of ship engine-room equipment were classified using a convolutional neural network (CNN). After constructing the image dataset of the ship engine room, the network was trained with a pre-trained CNN model. Classification performance of the trained model showed high reproducibility. Images were visualized with a class activation map. Although it cannot be generalized because the amount of data was limited, it is thought that if the data of each ship were learned through transfer learning, a model suitable for the characteristics of each ship could be constructed with little time and cost expenditure.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.28 no.2
• /
• pp.370-376
• /
• 2022

Smart ships related studies are being conducted in various fields owing to the development of technology, and an engine room patrol robot that can patrol the unmanned engine room is one such study. A patrol robot moves around the engine room based on the information learned through artificial intelligence and checks the machine normality and occurrence of abnormalities such as water leakage, oil leakage, and fire. Study on engine room patrol robots is mainly conducted on machine detection using artificial intelligence, however study on movement and control is insufficient. This causes a problem in that even if a patrol robot detects an object, there is no way to move to the detected object. To secure maneuverability to quickly identify the presence of abnormality in the engine room, this study experimented with whether a patrol robot can determine the shortest path by applying the A* algorithm. Data were obtained by driving a small car equipped with LiDAR in the ship engine room and creating a map by mapping the obtained data with SLAM(Simultaneous Localization And Mapping). The starting point and arrival point of the patrol robot were set on the map, and the A* algorithm was applied to determine whether the shortest path from the starting point to the arrival point was found. Simulation confirmed that the shortest route was well searched while avoiding obstacles from the starting point to the arrival point on the map. Applying this to the engine room patrol robot is believed to help improve ship safety.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.29 no.4
• /
• pp.389-394
• /
• 2023

In recent years, use of machine automation is rising in the industry. Ships also obtain machine condition information from sensor as digital information. However, on ships, crew members regularly surveil the engine room to check the condition of equipment and their information through analog gauges. This is a time-consuming and tedious process and poses safety risks to the crew while on surveillance. To address this, engine room surveillance using an autonomous mobile robot is being actively explored as a solution because it can reduce time, costs, and the safety risks for crew. Analog gauge reading using an autonomous mobile robot requires digitization for the robot to recognize the gauge value. In this study, image processing techniques were applied to achieve this. Analog gauge images were subjected to image preprocessing to remove noise and highlight their features. The center point, indicator point, minimum value and maximum value of the analog gauge were detected through image processing. Through the straight line connecting these points, the angle from the minimum value to the maximum value and the angle from the minimum value to indicator point were obtained. The obtained angle is digitized as the value currently indicated by the analog gauge through a formula. It was confirmed from the experiments that the digitization of the analog gauge using image processing was successful, indicating the equivalent current value shown by the gauge. When applied to surveillance robots, this algorithm can minimize safety risks and time and opportunity costs of crew members for engine room surveillance.

• Tribology and Lubricants
• /
• v.7 no.2
• /
• pp.13-21
• /
• 1991

선박은 사람이나 화물을 수상(水上) 운송수단의 목적으로 오랜 옛날부터 사용하여 왔으며, 금세기에 이르러서는 오랜 옛날부터 사용하여 왔으며, 금세기에 이르러서는 인류문화의 급속한 발전에 따라 주변생활이 가속화(Speed화), 합리화되어 문명의 혜택을 보다 많이 누리게 된 반면에, 국제간에 파생되는 여러가지 교환현상을 바탕으로 지구는 하나의 촌(村)으로 되어 유통경제를 지배하고 이용하는 운송수단으로 발전되어 왔다. 물질문명이 급속히 발전하는 작금, 선용추진 주 기관의 동향을 살펴보면, 1981년 세계에서 건조한 2000톤급 이상의 선박은 940여척으로 이중 13척이 증기 Turbine. 추진(推進), 나머지는 미끄럼 저속 디이젤 또는 중고속 디이젤 추진으로 되어있다. 이와 병행하여 선박에 쓰여지고 있는 각종 윤활제는 주기관으로서 디이젤 engine, Trubine engine, 가솔린 engine, 석유, engine, 선외기, 왕복동 증기기관, Journal계열로서 역전기, 추력베어링, 중간 베어링, 프로펠러베어링, 일반상선으 보조기계로서 발전기, 공기압축기, Boiler, 각종 Pump, 각종 Motor, 냉동기, 조타장치, Side Thrust, 갑판기계, 환기용 송풍기 및 통풍통, Oil 청정기, 수밀로(水蜜爐), 소화설비, 주기 개방용 천정 크레인, elevator, 어선으로서의 주 기관, 보조기기 등에 쓰여지고 있으며 이밖에 수중날개선 등에도 적용되는 등 실로 다양다종한 윤활제가 요구되고 있음에, 본 논고에서는 제목 건을 중심으로 한 선박과 윤활의 중요 Point만을 간추려 기술하고져 한다.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.25 no.1
• /
• pp.111-116
• /
• 2019

The fire detectors used in the engine rooms of ships offer only a slow response to emergencies because smoke or heat must reach detectors installed on ceilings, but the air flow in engine rooms can be very fluid depending on the use of equipment. In order to overcome these disadvantages, much research on video-based fire detection has been conducted in recent years. Video-based fire detection is effective for initial detection of fire because it is not affected by air flow and transmission speed is fast. In this paper, experiments were performed using images of smoke from a smoke generator in an engine room. Data generated using LBP and GLCM operators that extract the textural features of smoke was classified using SVM, which is a machine learning classifier. Even if smoke did not rise to the ceiling, where detectors were installed, smoke detection was confirmed using the image-based technique.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• 2019.05a
• /
• pp.230-231
• /
• 2019

This study is a development on condition based maintenance(CBM) technology which is a core item of future autonomous ships. It is developing to design & installation of condition monitoring system and acquisition & processing of data from ongoing ships for fault prediction & prognosis of engine in operation. The ultimate goal of this study is to develop a predicts and decision support software for marine engine faults. To do this, the FMEA and fault tree analysis of the main engine should be accompanied by the analysis of classification of system, identification of the components, the type of faults, and the cause and phenomenon of the failure. Finally, the CBM system solution software could predict and diagnose the failure of main engine through integrated analysis for bid-data of ongoing ships and engineering knowledge. Through this study, it is possible to pro-actively cope with abnormal signals of engine and to manage efficiently, and as a result, expected that marine accident and ship operation loss during navigation will be prevented in advance.