Data-Driven Fault Isolation in Linear Time-Invariant Systems: A Subspace Classification Approach
This work addresses the problem of fault isolation in linear systems without requiring a system model, which is important for practitioners who have only data available.
The paper proposes a data-driven fault isolation filter for linear systems that uses only fault-free input-output data, avoiding explicit system models. The method achieves fault isolation by reparameterizing the problem in a behavioral framework and provides conditions for fault discernibility that can be evaluated from data.
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, enabling a characterization of mutual fault discernibility in terms of fundamental system properties given a noise-free setting. In addition, the provided conditions can be evaluated using only the available data. Finally, a simulation study is conducted to demonstrate the effectiveness of the proposed method.