Traditional model-based diagnosis relies on constructing explicit system models, a process that can be laborious and expertise-demanding. In this paper, we propose a novel framework that combines concepts of model-based diagnosis with deep graph structure learning. This data-driven approach leverages data to learn the system's underlying structure and provide dynamic observations, represented by two distinct graph adjacency matrices. Our work facilitates a seamless integration of graph structure learning with model-based diagnosis by making three main contributions: (i) redefining the constructs of system representation, observations, and faults (ii) introducing two distinct versions of a self-supervised graph structure learning model architecture and (iii) demonstrating the potential of our data-driven diagnostic method through experiments on a system of coupled oscillators.

翻译：传统基于模型的诊断依赖于构建显式的系统模型，这一过程既繁琐又需要专业知识。本文提出了一种新颖框架，将基于模型诊断的概念与深度图结构学习相结合。这种数据驱动方法利用数据学习系统的底层结构，并通过两种不同的图邻接矩阵提供动态观测。我们的工作通过以下三点主要贡献实现了图结构学习与基于模型诊断的无缝融合：（i）重新定义系统表示、观测和故障的构建方式；（ii）引入两种不同版本的自监督图结构学习模型架构；（iii）通过在耦合振荡器系统上的实验，展示了我们数据驱动诊断方法的潜力。