Sensing technologies have been used to gather massive amounts of data to improve system reliability analysis with the use of deep learning. Their use has been mainly focused on specific components or for the whole system, resulting in a drawback when dealing with complex systems as the interactions among components are not explicitly taken into account. Here, we propose a system-level prognostics and health management framework based on geometrical deep learning where a system, its components with their interactions, and sensor data are represented as a graph. This enables reliability analysis at different hierarchical levels by means of (1) a system-level module for system health diagnosis and prognosis based on embeddings of the system’s learned features from a graph convolutional network; (2) a component-level module based on a deep graph convolutional network for health state diagnosis for the system’s components; (3) a component interactions module based on a graph convolutional network autoencoder that allows for the identification of interactions among components when the system is in a degraded state. The framework is exemplified via a case study involving a chlorine dioxide generation system, in which it is shown that integrating both components’ interactions and sensor data in the form of a graph improves health state diagnosis capabilities.

传感技术已被用来收集大量数据，以通过使用深度学习来改善系统可靠性分析。它们的使用主要集中在特定组件或整个系统上，由于未明确考虑组件之间的交互，因此在处理复杂系统时会出现缺陷。在这里，我们提出了基于几何深度学习的系统级预测和健康管理框架，其中，系统，其组件及其相互作用以及传感器数据以图形表示。这使得能够通过以下方式在不同的层次上进行可靠性分析：（1）一种基于系统知识的嵌入式系统级模块，该模块基于对图卷积网络的学习特征进行嵌入；（2）基于深度图卷积网络的组件级模块，用于诊断系统组件的健康状态；（3）基于图卷积网络自动编码器的组件交互模块，当系统处于降级状态时，该模块允许识别组件之间的交互。通过涉及二氧化氯生成系统的案例研究来举例说明该框架，其中显示了以图表形式集成组件的交互作用和传感器数据可以提高健康状态诊断能力。