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|Title:||A fault detection and diagnosis scheme for discrete nonlinear system using output probability density estimation||Authors:||Zhang, Y.
Probability density function
|Issue Date:||2008||Citation:||Zhang, Y.,Wang, Q.-G.,Lum, K.-Y. (2008). A fault detection and diagnosis scheme for discrete nonlinear system using output probability density estimation. Proceedings of the IEEE International Conference on Automation and Logistics, ICAL 2008 : 45-49. ScholarBank@NUS Repository. https://doi.org/10.1109/ICAL.2008.4636117||Abstract:||In this paper, a fault detection and diagnosis (FDD) scheme for a class of discrete nonlinear system fault using output probability density estimation is presented. Unlike classical FDD problems, the measured output of the system is viewed as a stochastic process and its square root probability density function (PDF) is modeled with B-spline functions, which leads to a deterministic space-time dynamic model including nonlinearities, uncertainties. A weighted average function is given as an integral form of the square root PDF along space direction, which leads a function only about time and can be used to construct residual signal. Thus, the classical nonlinear .lter approach can be used to detect and diagnose the fault in system. A feasible detection criterion is obtained at first, and a new adaptive fault diagnosis algorithm is further investigated to estimate the fault. The simulation example given demonstrates the effectiveness of the proposed approaches. © 2008 IEEE.||Source Title:||Proceedings of the IEEE International Conference on Automation and Logistics, ICAL 2008||URI:||http://scholarbank.nus.edu.sg/handle/10635/68797||ISBN:||9781424425020||DOI:||10.1109/ICAL.2008.4636117|
|Appears in Collections:||Staff Publications|
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