The ever-increasing functional density and complexity of the satellite systems, the harsh space flight environment, as well as the cost reduction measures that require less operator involvement are increasingly driving the need to develop new approaches for fault diagnosis and health monitoring (FD-HM). The data-driven FD-HM approaches use signal processing or data mining to obtain implicit information for the operating state of the system, which is good at monitoring systems extensively and shallowly and is expected to reduce the workload of the operators. However, these approaches for the FD-HM of the satellite system are driven primarily by the historical data and some static physical data, with little consideration for the simulation data, real-time data, and data fusion between the two, so it is not fully competent for the real-time monitoring and maintenance of the satellite in orbit. To ensure the reliable operation of the complex satellite systems, this paper presents a new physical–virtual convergence approach, digital twin, for FD-HM. Moreover, we present an FD-HM application of the satellite power system to demonstrate the effectiveness of the proposed approach.

中文翻译：

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基于数字孪生的复杂卫星系统故障诊断和健康监测方法

卫星系统不断增加的功能密度和复杂性、恶劣的太空飞行环境以及需要较少操作员参与的降低成本措施，越来越需要开发新的故障诊断和健康监测方法 (FD-HM) ）。数据驱动的FD-HM方法利用信号处理或数据挖掘来获取系统运行状态的隐含信息，擅长对系统进行广泛而浅层的监控，有望减少操作人员的工作量。然而，这些卫星系统FD-HM的方法主要由历史数据和一些静态物理数据驱动，很少考虑模拟数据、实时数据和两者之间的数据融合，所以它不能完全胜任对在轨卫星的实时监测和维护。为了确保复杂卫星系统的可靠运行，本文提出了一种新的物理-虚拟融合方法，即 FD-HM 的数字孪生。此外，我们提出了卫星电源系统的 FD-HM 应用，以证明所提出方法的有效性。