MOL is currently implementing the "Fleet Optimal Control Unified System (FOCUS) Project" intended to collect and apply big data related to navigation and engines of MOL Group-operated vessels at sea. As of August 2019, navigation and engine data collected from about 170 group vessels can be shared among personnel at sea and on shore.
In Part 1 of the FOCUS Project, MOL in May 2019 released the "Fleet Viewer" application, which enhances ship management. It collects nearly 6,000 items of sensing data from vessels in service at a high frequency (one-minute intervals), which allows sharing of the operational status of all equipment, vessel position, ocean and weather information, and so on, among vessels and shore-based personnel. It also features a user-friendly interface, which users can customize each function, and the customized content can be shared among them to help pass along knowledge and skills and thus boost operational efficiency.
In Part II of the FOCUS Project, MOL in October 2019 released the "Fleet Performance" application, which analyzes vessels in service with the goal of reducing fuel consumption. This application is intended to further reduce the environmental impact of the fleet by analyzing and comparing propulsion performance and so on among operated vessels.
Through "FOCUS", MOL looks forward to ongoing enhancement of ship management and operating efficiency by using condition-based maintenance (CBM)(*1), which relies on engine status diagnosis and failure sign diagnosis technologies, realizing "visualization at sea" through transmission of voice and visual information from vessels in operation to the shore side, and applying digital twin technology(*2).
(*1) Condition-Based Maintenance This refers to the conservation of status standards. Unlike time-based maintenance (TBM), which conventionally maintains equipment and components at fixed periods of time, CBM is a way to monitor equipment and components and maintain them at the appropriate intervals after identifying parts most likely to require maintenance.
(*2) Digital twin technology A way to digitally reproduce events in physical spaces. It builds twin-like simulation space into the system, and is used to change the design and optimize it through individual customization, as well as to diagnose status and identify signs of failure.
MOL is installing the next-generation "ClassNK CMAXS" engine status monitoring system developed by ClassNK Consulting Services Co., Ltd. on our operated vessels.
ClassNK CMAXS is intended to minimize vessel downtime by more quickly detecting abnormalities in electronically controlled engines, allowing swift preventive maintenance, and converting from time-based maintenance (TBM) to condition-based maintenance (CBM) to optimize lifecycle costs. CBM is a maintenance strategy that monitors the actual condition of equipment and components to analyze and decide what maintenance may be required, and ensures that maintenance is done at the appropriate time.
The accumulated operational know-how incorporated in "e-GICS" from engine maker Mitsui E&S Machinery Co., Ltd. and "LC-A" from IHI Power Systems Co., Ltd. (formerly Diesel United), as well as the "ClassNK CMAXS" machine learning-based algorithm even allow crewmembers on board to detect minor changes in engine condition that they might barely notice and to grasp signs of abnormal operation. Another factor that will enhance operating safety is the ability for communication between the crewmembers actually operating the equipment on board and the designers and engineers on the manufacturing side.
MOL plans to apply the project not only for main engine manufacturers, but also those who produce other equipment, as hundreds of pieces of equipment must work together to ensure the safe operation of the vessel.
This project focuses on the use of vibration sensors to monitor the condition of key auxiliary machinery, such as pumps and purifiers, on containerships, car carriers, and VLCCs using analysis software. MOL believes it will allow the condition of machinery to be monitored in a timely manner and abnormalities can be detected at an earlier stage, not only onboard, but also on the shore side. The project combines vibration sensor analysis technology developed by Asahi Kasei Engineering Corporation (AEC) for use in industrial plants on land with the "Fleet Monitor" platform for real-time communication between ship and shore as a part of the Ship Internet of Things (IoT) system.
In addition, under Nippon Kaiji Kyokai (ClassNK)’s "Joint Research and Development with Industries and Academic Partners" program, a study is now underway that incorporates an abrasion test of vibration sensors for marine propulsion main engines, power generators, and propeller shaft bearings, using a power generator engine at the MOL Technology Research Center. Data from actual ship operations is also being collected and analyzed as part of the evaluation.
The project is positioned as a part of the "ISHIN NEXT - MOL SMART SHIP PROJECT –," and the knowledge and expertise gained through the development and application of this technology will be fed back to various type of ships. This project also reflects MOL’s proactive stance in development of Ship IoT, propulsion plant technology, and enhanced vessel safety.
MOL announced the joint development along with SenseTime Japan Ltd. of a new vessel image recognition and recording system, and the system’s installation for demonstration testing aboard the cruise ship "Nippon Maru" operated by Mitsui O.S.K. Passenger Line, Ltd.
MOL, Mitsui E&S Shipbuilding Co., Ltd., Tokyo University of Marine Science and Technology (TUMST), and Akishima Laboratories (Mitsui Zosen) Inc. are working on a joint demonstration project related to the safety of automatic vessel berthing and unberthing, with the aim of developing a practical auto berthing/unberthing system.
Human errors account for about 80% of marine accidents. Automated and autonomous vessel operations are expected to significantly reduce human errors, and have the potential to achieve a large reduction in accidents. Berthing and unberthing are some of the most difficult phases of ship operation, in which autonomous operations would be of great benefit. We launched this project with the goal of realizing such a system. This auto berthing and unberthing demonstration project identified technical issues by demonstrating autonomous operation not only with a simulator, but also with an actual vessel and study ways to achieve practical use. First, we gained useful data through a demonstration test using the TUMST training ship Shioji Maru from December 2018 to February 2019. A future test will involve a large coastal ferry.
(*) The project was selected for Japan’s Ministry of Land, Infrastructure, Transportation and Tourism (MLIT). Through this project, MLIT aims to achieve practical use of autonomous vessels by 2025.
MOL has teamed up with MOL Techno-Trade, Ltd., the National Institute of Maritime, Port and Aviation Technology, and Tokyo University of Marine Science and Technology (TUMSAT) to conduct joint research toward the development of an advanced navigation support system.
Specifically, the joint study aims to develop a navigation support system that introduces the concept of Obstacle Zone by Target (OZT), a ship collision risk index, leading to the application of technologies such as the Automatic Radar Plotting Aid (ARPA).
Officers must first be able to see an object, and then judge whether that object poses a risk to the vessel. If that is the case, the vessel must take some action to avoid the risk, such as veering, slow steaming, and so on. The OZT algorithm supports a mariner’s ability to spot those objects and determine whether they are potential risks.
Conventional collision avoidance is a highly skilled process based on maritime officers’ experience and knowledge. However, by utilizing a navigation support system incorporating OZT, the vessel collision avoidance algorithm, a vessel can find an area where it can navigate safely. In addition, a display showing the OZT from the perspective of the bridge allows mariners to determine the positions of nearby vessels and decide which ones present risks.
MOL continues research and development toward realization of augmented reality (AR) technology, which overlaps displays of information from the Automatic Identification System (AIS) and radar, camera images from the bridge, and advanced navigation support systems, with the long-range goal of autonomous sailing and automatic collision prevention utilizing OZT.
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MOL, Furuno Electric Co., Ltd., and MOL Techno-Trade, Ltd. have jointly developed a system that supports ship operation during voyages using augmented reality (AR) technology.
The system shows other ships sailing near the vessel and landmarks like buoys at sea on tablets and other displays, based on data from the Automatic Identification System (AIS). Images of seascapes taken from the bridge can also be shown on the same tablets, and these images will overlap with AR to provide visual support to crewmembers operating ships and keeping watch during voyages.
MOL started installing the system on in-service vessels in 2019 as part of its ongoing quest for technologies that enhance vessel operating safety.
MOL has introduced the Vessel View VR, a virtual reality system that allows for virtual ship visits, based on technology developed by NURVE, Inc.
It takes a long time to tour every area of huge vessels. Even for an in-person visitor, it’s difficult to take photos or make notes on ships like tankers that carry hazardous or flammable cargoes, due to fire safety-related restrictions on electronic devices, especially when the visitor is on deck. There are also fewer opportunities to visit ships that ply extremely long routes. Therefore, the use of "Vessel View VR," which enables virtual ship visits "anytime, anywhere," allows for safe visits to ships that are transporting dangerous cargoes, in addition to relaxing past restrictions on ship visits.
Currently, we can make virtual visits to a car carrier, a VLCC, an LNG carrier, and a ferry with this tool, and plan to introduce it aboard a containership and other types of vessels. Virtual ship visits can be an effective way to promote MOL’s service to customers, build safety awareness, and train crewmembers and other employees, as MOL continues to work toward becoming the world leader in safe operation.
MOL has developed a mariner safety education tool that uses virtual reality (VR) goggles and VR technology created by Tsumiki Seisaku Co., Ltd.
The tool uses VR technology to replicate onboard industrial accidents caused by unsafe behavior, offering a new level of realism and immersion, and also allows for experience and training on situations that are difficult to replicate in conventional training. It works with easily portable VR goggles, so seafarers can train safely regardless whether they are onboard or in an office or training center.
Currently, MOL provides contents such as falling accidents, the fall of cargo suspended from a crane, and fire response measures as examples of onboard accidents, and continues to expand the lineup while confirming the effectiveness of the training sessions. MOL will introduce the tool to more vessels while expanding the range of simulated experiences to provide more training in onboard safety, leading to the eradication of onboard accidents and meeting customer demand for safer, more reliable transport services.
MOL announced a joint success with Japan Radio Co., Ltd. and JSAT MOBILE Communications Inc. in building a network that shares data recorded in Voyage Data Recorder (VDR) by using Fleet Xpress provided by Inmarsat, a high-capacity, high-speed satellite telecommunication service.
The Meteorological Service Act requires Japan-registry vessels to submit regular ocean weather data reports to the Japan Meteorological Agency. Vessels report to the agency using "manual measurement/manual transmission" measures, based on ocean weather data personally observed by officers with eyes onboard during a voyage.
MOL, Sky Perfect JSAT Corporation and Furuno Electric Co., Ltd. are jointly developing observation equipment that would allow automatic observation and transmission of ocean weather information instead of relying on the current manual procedures. This development project is aimed at increasing the accuracy of ocean weather forecasting and contributing to vessels operating safety by dramatically increasing the availability and volume of weather observation data.
It is worth noting that the project was selected by Japan’s Ministry of Land, Infrastructure, Transport and Tourism (MLIT) as part of the ministry’s projects for "2017 MLIT R&D Support Projects for Advanced Safety Technology of Vessels," and its technology development is advancing under a five-year plan starting in 2017.
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