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系统模型，这一过程通常耗时且需要专业知识。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潜在结构，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个主要贡献，促进了图结构学习与基于模型诊断的无缝融合：（i）重新定义了系统表征、观测和故障的构造；（ii）引入了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。