用数据驱动方法构建热液压过程数字孪生，实现故障实时检测与参数在线估计。
Data-Driven Supervision of a Thermal-Hydraulic Process Towards a Physics-Based Digital Twin
- 结合数值模拟与机器学习，实现参数变化的在线检测与估计。
- 在单次参数突变场景下，定位精度和值更新准确率均表现良好。
- 适合工业过程监控、故障诊断领域研究者参考使用。
生产过程的实时监控是多个行业面临的共同挑战，旨在通过监测设备部件并进行预测性维护来保障安全、连续生产及高效率。随着物理系统仿真工具和数据驱动机器学习模型的发展，设计高效系统监控的数值工具成为可能。在此背景下，数字孪生概念提供了解决方案的合适框架。本文旨在为热液压过程的故障检测与诊断开发一个专用数字孪生系统。基于系统数值模拟与机器学习方法，提出多个模块用于检测过程参数变化并在线估计其值。所提出的故障检测与诊断算法在特定测试场景中得到验证，针对系统中单次偶然的参数变化事件。数值结果表明，该方法在参数变化定位和数值更新方面具有较高准确性。
原文摘要 · Abstract (English)
The real-time supervision of production processes is a common challenge across several industries. It targets process component monitoring and its predictive maintenance in order to ensure safety, uninterrupted production and maintain high efficiency level. The rise of advanced tools for the simulation of physical systems in addition to data-driven machine learning models offers the possibility to design numerical tools dedicated to efficient system monitoring. In that respect, the digital twin concept presents an adequate framework that proffers solution to these challenges. The main purpose of this paper is to develop such a digital twin dedicated to fault detection and diagnosis in the context of a thermal-hydraulic process supervision. Based on a numerical simulation of the system, in addition to machine learning methods, we propose different modules dedicated to process parameter change detection and their on-line estimation. The proposed fault detection and diagnosis algorithm is validated on a specific test scenario, with single one-off parameter change occurrences in the system. The numerical results show good accuracy in terms of parameter variation localization and the update of their values.
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