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.

本文提出了一种新颖的框架，将基于模型的诊断与深度图结构学习的概念相结合。通过利用数据学习系统的底层结构和提供动态观察，该方法实现了图结构学习与基于模型的诊断的无缝集成，并通过在耦合振荡器系统上的实验证明了其数据驱动诊断方法的潜力。