1. Proximal-based recursive implementation for model-free data-driven fault diagnosis.
- Author
-
Noom, Jacques, Soloviev, Oleg, and Verhaegen, Michel
- Subjects
- *
FAULT diagnosis , *LINEAR time invariant systems , *SYSTEM dynamics , *COMORBIDITY , *LINEAR systems , *SYSTEM identification - Abstract
We present a novel problem formulation for model-free data-driven fault diagnosis, in which possible faults are diagnosed simultaneously to identifying the linear time-invariant system. This problem is practically relevant for systems whose model cannot be identified reliably prior to diagnosing possible faults, for instance when operating conditions change over time, when a fault is already present before system identification is carried out, or when the system dynamics change due to the presence of the fault. A computationally attractive solution is proposed by solving the problem using unconstrained convex optimization, where the objective function consists of three terms of which two are non-differentiable. An additional recursive implementation based on a proximal algorithm is presented in order to solve the optimization problem online. The numerical results on a buck converter show the application of the proposed solution both offline and online. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF