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.
- Feedforward neural network
- Model-based fault detection
- Parametric fault
- Recursive estimation
- Subspace system identification
Gil, P., Santos, F., Palma, L., & Cardoso, A. (2015). Recursive subspace system identification for parametric fault detection in nonlinear systems. Applied Soft Computing, 37, 444-455. https://doi.org/10.1016/j.asoc.2015.08.036