  The demand for condition-based and predictive maintenance is rising across
industries, especially for remote, high-value, and high-risk assets. In this
article, the diagnostic digital twin concept is introduced, discussed, and
implemented for a floating offshore turbine. A diagnostic digital twin is a
virtual representation of an asset that combines real-time data and models to
monitor damage, detect anomalies, and diagnose failures, thereby enabling
condition-based and predictive maintenance. By applying diagnostic digital
twins to offshore assets, unexpected failures can be alleviated, but the
implementation can prove challenging. Here, a diagnostic digital twin is
implemented for an operational floating offshore wind turbine. The asset is
monitored through measurements. Unsupervised learning methods are employed to
build a normal operation model, detect anomalies, and provide a fault
diagnosis. Warnings and diagnoses are sent through text messages, and a more
detailed diagnosis can be accessed in a virtual reality interface. The
diagnostic digital twin successfully detected an anomaly with high confidence
hours before a failure occurred. The paper concludes by discussing diagnostic
digital twins in the broader context of offshore engineering. The presented
approach can be generalized to other offshore assets to improve maintenance and
increase the lifetime, efficiency, and sustainability of offshore assets.
