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Proximal-based recursive implementation for model-free data-driven fault diagnosis.
- Source :
-
Automatica . Jul2024, Vol. 165, pN.PAG-N.PAG. 1p. - Publication Year :
- 2024
-
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]
Details
- Language :
- English
- ISSN :
- 00051098
- Volume :
- 165
- Database :
- Academic Search Index
- Journal :
- Automatica
- Publication Type :
- Academic Journal
- Accession number :
- 177223241
- Full Text :
- https://doi.org/10.1016/j.automatica.2024.111656