Delay-dependent fault detection and diagnosis using B-spline neural networks and nonlinear filters for time-delay stochastic systems

  • Authors:

  • Tao Li;Yang Yi;Lei Guo;Hong Wang

  • Affiliations:

  • Research Institute of Automation Southeast University, Nanjing, China;Research Institute of Automation Southeast University, Nanjing, China;Beihang University, The School of Instrument Science and Opto-Electronics Engineering, 100083, Beijing, China;UMIST, Department of Electrical Engineering and Electronics, PO Box 88, M60 1QD, Manchester, UK

  • Venue:
  • Neural Computing and Applications - Special Issue: Neural networks for control, robotics and diagnostics
  • Year:
  • 2008

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Abstract

A new fault detection and diagnosis (FDD) scheme is studied in this paper for the continuous-time stochastic dynamic systems with time delays, where the available information for the FDD is the input and the measured output probability density functions (PDFs) of the system. The square-root B-spline neural networks is used to formulate the output PDFs with the dynamic weightings. As a result, the concerned FDD problem can be transformed into a robust FDD problem subjected to a continuous time uncertain nonlinear system with time delays. Delay-dependent criteria to detect and diagnose the system fault are provided by using linear matrix inequality (LMI) techniques. It is shown that this new criterion can provide higher sensitivity performance than the existing result. Simulations are given to demonstrate the efficiency of the proposed approach.