Reygar wins funding for performance monitoring


Reygar, a vessel monitoring and control solutions provider, has won InnovateUK funding to develop a range of new features for its BareFLEET product in collaboration with Singapore-based designer, builder, owner, and operator of high-speed aluminum craft, Penguin International Limited.

The project, dubbed FleetVision, will build on Reygar’s BareFLEET technology to offer live feedback on aspects of vessel performance, leveraging Penguin’s experience in shipbuilding and ship management.

Machine learning tools will be jointly developed to identify operating efficiency and cost reduction opportunities and to monitor machinery health, alongside adaptations that will enable more vessel types to benefit from the system.

Penguin designs and builds a range of aluminum workboats that it also owns and operates. Since 1995, Penguin has delivered more than 200 aluminum vessels to ship owners and is the world’s biggest builder of multi-role crew boats.

Both Reygar and Penguin envisage long-term, mutual benefits, with the project acting as a potential launch pad for access to new technology and target markets.

James Tham, Penguin’s managing director, sees potential in the application of data-driven performance monitoring technology to enhance efficiency and emission reductions for commercial high-speed vessels.

“FleetVision represents the coming together of proven expertise and experience in real-time remote monitoring technology and the design, construction and operation of efficient, human-centric high-speed workboats,” he said. “The outcome will be an intelligent performance analytics and decision support tool, developed by experienced practitioners, for sustainable high-speed vessel operations.”

“We are passionate about helping fleet operators make better, more informed decisions to reduce fuel consumption and emissions,” said Reygar CEO Chris Huxley-Reynard.

“Live feedback on vessel performance means that a range of cost, fuel and emissions saving opportunities can be seen and acted upon in real time, optimizing operations both onboard and from the shore. Leveraging machine learning to help identify trends in machinery health and vessel performance also improves availability and supports the achievement of perating efficiency goals.”

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