Tootfinder

Data-Driven Fault Isolation in Linear Time-Invariant Systems: A Subspace Classification Approach
Mohammad Amin Sheikhi, Gabriel de Albuquerque Gleizer, Peyman Mohajerin Esfahani, Tam\'as Keviczky
https://arxiv.org/abs/2509.01347

Data-Driven Fault Isolation in Linear Time-Invariant Systems: A Subspace Classification Approach
We study the problem of fault isolation in linear systems with actuator and sensor faults within a data-driven framework. We propose a nullspace-based filter that uses solely fault-free input-output data collected under process and measurement noises. By reparameterizing the problem within a behavioral framework, we achieve a direct fault isolation filter design that is independent of any explicit system model. The underlying classification problem is approached from a geometric perspective, en…

Identifying Network Structure of Linear Dynamical Systems: Observability and Edge Misclassification
Jaidev Gill, Jing Shuang Li
https://arxiv.org/abs/2509.14065 https://

Identifying Network Structure of Linear Dynamical Systems: Observability and Edge Misclassification
This work studies the limitations of uniquely identifying a linear network's topology from partial measurements of its nodes. We show that the set of networks that are consistent with the measurements are related through the nullspace of the observability matrix for the true network. In doing so, we illustrate how potentially many networks are fully consistent with the measurements despite having topologies that are structurally inconsistent with each other, an often neglected consideration in …