Sensed data from high-value engineering systems is being increasingly exploited to optimise their operation and maintenance. In aerospace, returning all measured data to a central repository is prohibitively expensive, often causing useful, high-value data to be discarded. The ability to detect, prioritise and return useful data on asset and in real-time is vital to move toward more sustainable maintenance activities. We present a data-driven solution for on-line detection and prioritisation of anomalous data that is centrally processed and used to update individualised digital twins (DT) distributed onto remote machines. The DT is embodied as a convolutional neural network (CNN) optimised for real-time execution on a resource constrained gas turbine monitoring computer. The CNN generates a state prediction with uncertainty, which is used as a metric to select informative data for transfer to a remote fleet monitoring system. The received data is screened for faults before updating the weights on the CNN, which are synchronised between real and virtual asset. Results show the successful detection of a known in-flight engine fault and the collection of data related to high novelty pre-cursor events that were previously unrecognised. We demonstrate that data related to novel operation are also identified for transfer to the fleet monitoring system, allowing model improvement by retraining. In addition to these industrial dataset results, reproducible examples are provided for a public domain NASA dataset. The data prioritisation solution is capable of running in real-time on production-standard low-power embedded hardware and is deployed on the Rolls-Royce Pearl 15 engines.

中文翻译：

---

用于健康监测的分布式数字孪生：资源受限的航空发动机机队管理

来自高价值工程系统的感测数据被越来越多地利用来优化其操作和维护。在航空航天领域，将所有测量数据返回到中央存储库的成本极其昂贵，通常会导致有用的高价值数据被丢弃。实时检测、优先排序和返回资产有用数据的能力对于实现更可持续的维护活动至关重要。我们提出了一种数据驱动的解决方案，用于在线检测异常数据并确定其优先级，该解决方案经过集中处理并用于更新分布到远程机器上的个性化数字孪生（DT）。 DT 体现为卷积神经网络 (CNN)，针对在资源受限的燃气轮机监控计算机上实时执行进行了优化。 CNN 生成具有不确定性的状态预测，用作选择信息数据传输到远程车队监控系统的指标。在更新 CNN 上的权重之前，会筛选接收到的数据中的错误，这些权重在真实资产和虚拟资产之间同步。结果显示，成功检测到已知的飞行中发动机故障，并收集了与先前未识别的高度新颖的前兆事件相关的数据。我们证明，与新颖操作相关的数据也可以被识别并传输到车队监控系统，从而可以通过再训练来改进模型。除了这些工业数据集结果之外，还为公共领域 NASA 数据集提供了可重复的示例。该数据优先级解决方案能够在生产标准的低功耗嵌入式硬件上实时运行，并部署在劳斯莱斯 Pearl 15 发动机上。