Proximal-based recursive implementation for model-free data-driven fault diagnosis
J. Noom (TU Delft - Team Shengling Shi)
O.A. Soloviev (TU Delft - Team Shengling Shi, Flexible Optical B.V.)
M.H.G. Verhaegen (TU Delft - Team Shengling Shi)
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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.
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