Recursive subspace system identification for parametric fault detection in nonlinear systems

P. Gil, F. Santos, L. Palma, Alberto Cardoso

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)


This work addresses the problem of detecting parametric faults in nonlinear dynamic systems by extending an eigenstructure based technique to a nonlinear context. Two local state-space models are updated online based on a recursive subspace system identification technique. One of the models relies on input-output real-time data collected from the plant, while the other is updated using data generated by a neural network predictor, describing the nonlinear plant behaviour in fault-free conditions. Parametric faults symptoms are generated based on eigenvalues residuals associated with two linear state-space model approximators. The feasibility and effectiveness of the proposed framework are demonstrated through two case studies.
Original languageEnglish
Pages (from-to)444-455
JournalApplied Soft Computing
Publication statusPublished - Dec 2015


  • Feedforward neural network
  • Model-based fault detection
  • Parametric fault
  • Recursive estimation
  • Subspace system identification


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