Abstract
This paper presents a novel approach for the change detection of the output probability density functions for dynamic stochastic systems. Using the B-splines neural network, the measured probability density functions of the system output are represented by a set of weights which are functions of the control inputs to the system. This leads to a unique expression of the dynamic characteristics of the output probability density functions for the system. An observer based fault detection is developed which detects any unexpected changes of the parameters in the dynamic part of the system. An application to the detection of unexpected changes of particle size in paper-making is included to demonstrate the use of the proposed algorithm and desired results have been obtained.
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