Recursive subspace system identification for parametric fault detection in nonlinear systems



tThis work addresses the problem of detecting parametric faults in nonlinear dynamic systems by extend-ing an eigenstructure based technique to a nonlinear context. Two local state-space models are updatedonline based on a recursive subspace system identification technique. One of the models relies oninput–output real-time data collected from the plant, while the other is updated using data generated bya neural network predictor, describing the nonlinear plant behaviour in fault-free conditions. Parametricfaults symptoms are generated based on eigenvalues residuals associated with two linear state-spacemodel approximators. The feasibility and effectiveness of the proposed framework are demonstratedthrough two case studies.

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iCIS - Intelligent Computing in the Internet of Services


Applied Soft Computing, Vol. 37, pp. 444-455, Elsevier 2015


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Year 2017 : 1 citations

 Belchior, Carlos Alberto C., et al. "Sensor-fault tolerance in a wastewater treatment plant by means of ANFIS-based soft sensor and control reconfiguration." Neural Computing and Applications: 1-12.

Year 2016 : 1 citations

 Jiang, Yuchen, et al. "Study on recent developments of residual generation design approach based on available process measurements." Industrial Technology (ICIT), 2016 IEEE International Conference on. IEEE, 2016.