Data-Driven Sensor Fault Diagnosis with Proven Guarantees using Incrementally Stable Recurrent Neural Networks
Abstract
Robust Recurrent Neural Networks (R-RENs) are a class of neural networks that have built-in system-theoretic robustness and incremental stability properties. In this manuscript, we leverage these properties to construct a data-driven Fault Detection and Isolation (FDI) method for sensor faults with proven performance guarantees. The underlying idea behind the scheme is to construct a bank of multiple R-RENs (acting as fault isolation filters), each with different levels of sensitivity (increased or decreased) to faults at different sensors. That is, each R-REN is designed to be specifically sensitive to faults occurring in a particular sensor and robust against faults in all the others. The latter is guaranteed using the built-in incremental stability properties of R-RENs. The proposed method is unsupervised (as it does not require labeled data from faulty sensors) and data-driven (because it exploits available fault-free input-output system trajectories and does not rely on dynamic models of the system under study). Numerical simulations on a roll-plane model of a vehicle demonstrate the effectiveness and practical applicability of the proposed methodology.
Keywords
Cite
@article{arxiv.2504.19688,
title = {Data-Driven Sensor Fault Diagnosis with Proven Guarantees using Incrementally Stable Recurrent Neural Networks},
author = {Farhad Ghanipoor and Carlos Murguia and Giancarlo Ferrari Trecate and Nathan van de Wouw},
journal= {arXiv preprint arXiv:2504.19688},
year = {2025}
}