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A NEW UNIVERSAL MONITORING PLATFORM FOR INFRASTRUCTURES

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New tool enables inspection of structural damage in real time

France-based start-up MORPHOSENSE unveiled an innovative platform that monitors the fatigue, damage and residual lifespan of large engineering structures in real time. The new solution ensures continuity and efficiency of operation, while minimising maintenance costs.

Industrial Technologies
Security

Ageing degradation, extreme weather and seismic damage lead to serious threats to vulnerable infrastructures and result in both economic and human losses. Structural health monitoring of infrastructures and civil engineering structures have become mandatory, requiring real-time, continuous monitoring and damage detection with minimal manpower involvement. With support from the EU-funded MORPHOSAVE project, MORPHOSENSE delivered a tailored monitoring solution for various types of structures: oil and gas platforms, bridges, dams, offshore wind turbines and large vessels. “Our innovative, vibration-based, structural health monitoring system called NEURON can continuously and simultaneously monitor the static and dynamic properties of structures,” notes Alexandre Paleologue, CEO at MORPHOSENSE and MORPHOSAVE coordinator. The NEURON technology is part of the MORPHOSENSE digital-twin global solution AXON, which is updated from real-time data and uses artificial intelligence convergence algorithms on physics-based models. “Combining synchronised static and dynamic data stemming from NEURON and an interoperability function that allows integrating data from off-the-shelf sensors (weather stations, strain gauges, crack meters, cameras and LiDAR systems) led us to create a real-time digital twin that assesses the fatigue, damage and residual service life of structures equipped with the NEURON system,” explains Paleologue.

Improved monitoring solutions

Structure inspection and maintenance are currently based on periodic on-site visual inspections, which sometimes involve use of sensors or drones, but remain limited in their ability to check the entire structure. For example, on-site inspection of offshore wind structures cannot provide information on underwater areas that are particularly prone to structural vulnerabilities. “The MORPHOSENSE solution allows continuous remote monitoring of the structure instead of schedule-based, non-data-driven approaches. It adapts the number of sensors and their distribution over the structure to the hot spots identified by simulations of a physical model. Furthermore, the model allows us to view the structure’s behaviour at all points, independently of the sensor position, enabling us to work both locally and globally,” Paleologue observes. Another way to monitor a structure’s health status is to track deviations from its usual behaviour in the measured data through data-driven models. Despite such models’ ability to quickly detect any deviation from a structure’s standard behaviour, they cannot provide a physical understanding of the cause of the problem. It is, therefore, difficult to predict a proper maintenance response. “Our digital twin solution is built around a measurement-constrained digital model that provides detailed information on any new changes to the integrity of the structure. If the depth of the float or the mooring line changes, the digital model can immediately identify this change and provide an accurate notification to the operator,” explains Paleologue.

Flexibility in use and high expected gains

MORPHOSENSE produced several proof-of-concept systems for the offshore wind market. “Our digital twin solution could help increase the profitability of offshore wind farms, even with a target levelised cost of energy as low as EUR 45/MWh by 2040. The estimated return on investment is 15 % over a 20-year lifespan of a wind park,” says Paleologue. “In the future, we aim to develop a generic digital twin solution that enables the integration of any reduced-order model tool available on the market.”

Keywords

MORPHOSAVE, structure, MORPHOSENSE, inspection, offshore wind, digital twin, structural health monitoring, data-drivel model

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