Data-driven design of fault detection and isolation systems subject to Hammerstein nonlinearity

@article{Wang2015DatadrivenDO,
  title={Data-driven design of fault detection and isolation systems subject to Hammerstein nonlinearity},
  author={Yulei Wang and Bingzhao Gao and Hong Chen},
  journal={2015 American Control Conference (ACC)},
  year={2015},
  pages={214-219}
}
This paper is concerned with data-driven design of fault detection and isolation (FDI) systems subject to Hammerstein nonlinearity, a static nonlinearity in the front of inputs. Specifically, the design of residual generation is then formulated as to solve a convex optimization problem by combining ideas from the over-parameterization and least squares support vector machines (LS-SVMs), and thus provides residual signals directly from process data. To solve the multiply-outputs (MOs) problem, a… CONTINUE READING

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