Proximal-based recursive implementation for model-free data-driven fault diagnosis

Jacques Noom*, Oleg Soloviev, Michel Verhaegen

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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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.

Original languageEnglish
Article number111656
Number of pages11
Publication statusPublished - 2024


  • Fault detection and isolation
  • System identification


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