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.

本文介绍、讨论和实施了一种用于浮动离岸风力涡轮机的诊断数字孪生概念，通过实时数据和模型相结合来监测损伤、检测异常和诊断故障，从而实现基于条件和预测的维护。通过将诊断数字孪生应用于离岸设备，可以减轻意外故障，但实施可能具有挑战性。本文实现了一个用于运行中的浮动离岸风力涡轮机的诊断数字孪生，通过测量监控该资产，采用无监督学习方法建立正常运行模型，检测异常并提供故障诊断，并通过短信发送警告和诊断结果，在虚拟现实界面中可以得到更详细的诊断结果。诊断数字孪生在故障发生前数小时成功检测到异常情况，并以大的置信度进行诊断。文章最后讨论了诊断数字孪生在离岸工程的广泛背景下的应用，该方法可以推广到其他离岸设备，提高设备的维护水平，提高离岸设备的寿命、效率和可持续性。