Optimizing mooring performance and manage risk with data technology

Richard Campbell, CEO, Applied Fiber
Real time data is now a critical component to manage high value assets across many industries. It allows operators and owners to monitor multiple complex systems remotely and has led to advancements in performance optimization, improved reliability, and avoidance of failures. Artificial intelligence and machine learning leverage this data to make further advancements in design and preventive maintenance. Collectively, these advancements have led to “smart” maintenance and the prevention of catastrophic failures, both resulting in costs savings. FOW relies on turbines and structures with extensive levels of sensing instrumentation, however they are in turn anchored with mooring systems that have changed little in the last decades. Currently the only way to predict performance of the mooring system is with the use of modeling techniques that at best give an approximation of field performance. These limitations drive engineering uncertainty which is typically addressed through higher design factors, increased inspections, or early retirement; all significantly drive costs and are not effective at addressing the stacking of factors which is typical in field use. This paper will give an overview of technology that brings state of the art health monitoring and data gathering to FOW mooring systems. The data will provide stake holders the information needed to create a step change in the understanding of FOW mooring systems resulting in safer, more reliable, and cost-effective systems.