Diagnostic features space construction using Volterra kernels wavelet transforms

@article{Medvedew2017DiagnosticFS,
  title={Diagnostic features space construction using Volterra kernels wavelet transforms},
  author={Andrey Medvedew and Oleksandr Fomin and Vitaliy D. Pavlenko and Viktor O. Speranskyy},
  journal={2017 9th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS)},
  year={2017},
  volume={2},
  pages={1077-1081}
}
In this paper the problem of improving the reliability of nonlinear dynamic objects fault diagnosing is presented. Model-based diagnostics nonparametric identification method is used. Diagnostic models are constructed on the base of Volterra kernels wavelet transforms. The effectiveness of the suggested diagnostic models based on Volterra kernels wavelet transforms is analyzed on the basis of simulation model of nonlinear dynamic object.