Picture of smart phone in human hand

World leading smartphone and mobile technology research at Strathclyde...

The Strathprints institutional repository is a digital archive of University of Strathclyde's Open Access research outputs. Strathprints provides access to thousands of Open Access research papers by University of Strathclyde researchers, including by Strathclyde researchers from the Department of Computer & Information Sciences involved in researching exciting new applications for mobile and smartphone technology. But the transformative application of mobile technologies is also the focus of research within disciplines as diverse as Electronic & Electrical Engineering, Marketing, Human Resource Management and Biomedical Enginering, among others.

Explore Strathclyde's Open Access research on smartphone technology now...

Fault detection and diagnosis for stochastic distribution systems using a rational square-root approximation model

Yao, L.N. and Wang, H. and Yue, H. and Zhou, J. (2006) Fault detection and diagnosis for stochastic distribution systems using a rational square-root approximation model. In: 45th IEEE Conference on Decision and Control, 2006-12-13 - 2006-12-15.

Full text not available in this repository. (Request a copy from the Strathclyde author)


Stochastic distribution control (SDC) systems are a group of systems where the outputs considered are the measured probability density functions (PDFs) of the system output whilst subjected, to a normal crisp input. The purpose of the fault detection and diagnosis of such systems is to use the measured input and the system output PDFs to obtain possible faults information of the system. In this paper the rational square-root B-spline model is used to represent the dynamics between the output PDF and the input, where comparisons of such a model with respect to other approximation models are discussed first. This is then followed by the novel design of a nonlinear adaptive observer-based fault diagnosis. algorithm so as to diagnose the fault in the dynamic part of such systems. Convergency analysis is performed for the error dynamics raised from the fault detection and diagnosis phase and a simulated example is given to illustrate the efficiency of the proposed algorithm.