Innovative Hull monitoring concept from light structures receives ABS approval

ABS has issued an approval in principle to Light Structures for its new hull monitoring concept, ARGUS-VM.
The ARGUS-VM concept received a SMART Tier 1 AIP from ABS, which provides an industry recognition that the product complies with the functional and system requirements listed in the ABS Guide for Smart Functions for Marine Vessels and Offshore Units.
Unlike traditional hull monitoring systems that require physical strain sensors, ARGUS-VM provides virtual measurements of hull responses using vessel data from existing onboard systems. This makes it a scalable alternative for a wider range of shipowners and shipbuilders looking to monitor structural performance without additional onboard instrumentation.
“Our AIP for Light Structures is a continuation of the ABS activities to approve in principle new technologies that provide informed and actionable insights to vessel owners and operators on the structural health condition of their vessels. These insights, underpinned by real operational data, provide crew and support personnel with key information to aid in decision making,” said Gareth Burton, ABS Senior Vice President, Global Engineering.
“We are proud to be the first company to receive a SMART (SHM) Tier 1 approval in principle from ABS, which validates the core concept behind ARGUS-VM,” said Niklas Hallgren, CEO of Light Structures. “This recognition affirms our role as pioneers in the field of structural health monitoring and reflects our continued commitment to pushing the boundaries of what SHM can offer. ARGUS-VM demonstrates that reliable structural health indicators can be derived through software-based approaches that leverage existing vessel operational data. While sensorless systems like ARGUS-VM do not deliver the high-fidelity insight of fully sensor-equipped SHM solutions, they provide a powerful and scalable entry point for owners to integrate structural health awareness into their operations, and a natural step toward deeper structural insights provided by sensor-driven implementations.”
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