One of the challenges in twinned systems is ensuring the digital twin remains a valid representation of the system it twins. Depending on the type of twinning occurring, it is either trivial, such as in dashboarding/visualizations that mirror the system with real-time data, or challenging, in case the digital twin is a simulation model that reflects the behavior of a physical twinned system. The challenge in this latter case comes from the fact that in contrast to software systems, physical systems are not immutable once deployed, but instead they evolve through processes like maintenance, wear and tear or user error. It is therefore important to detect when changes occur in the physical system to evolve the twin alongside it. We employ and reuse validation techniques from model-based design for this goal. Model validation is one of the steps used to gain trust in the representativeness of a simulation model. In this work, we provide two contributions: (i) we provide a generic approach that, through the use of validation metrics, is able to detect anomalies in twinned systems, and (ii) we demonstrate these techniques with the help of an academic yet industrially relevant case study of a gantry crane such as found in ports. Treating anomalies also means correcting the error in the digital twin, which we do with a parameter estimation based on the historical data.

翻译：孪生系统面临的核心挑战之一在于确保数字孪生始终是其对应系统的有效表征。根据孪生类型的不同，这一任务可能极为简单（例如通过实时数据镜像系统状态的仪表盘/可视化界面），也可能极具挑战性（例如数字孪生为反映物理孪生系统行为的仿真模型）。后者的挑战性源于物理系统与软件系统的本质差异：物理系统在部署后并非一成不变，而是会通过维护、磨损或用户误操作等过程持续演化。因此，及时检测物理系统的变化并使孪生模型同步演进至关重要。为实现这一目标，我们采用并复用了基于模型设计的验证技术。模型验证是建立仿真模型代表性可信度的关键环节之一。本研究作出两项贡献：（i）提出一种通用方法，通过运用验证指标检测孪生系统中的异常现象；（ii）以港口门式起重机这一兼具学术价值与工业相关性的案例，演示该技术的实际应用。异常处理还需修正数字孪生中的误差，我们基于历史数据通过参数估计方法实现误差校正。