  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.

Artificial Intelligence;Machine Learning