Abstract
This paper investigates the problem of fault detection and diagnosis (FDD) problem for non-Gaussian singular stochastic distribution control (SDC) systems via the output probability density functions(PDFs). The PDFs can be approximated by using square-root B-spline expansion, via this expansions to represent the dynamics weighting systems between the system input and the weights related to the output PDFs. In this work, an optimal fault detection and diagnosis algorithm is presented by introducing the parameter-updating. When the fault occurs, an adaptive network parameter-updating law is designed to approximated the fault. Finally, the simulation result are given to show that the approach can detect fault and estimate the size of fault.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.