Diagnosis of sensor failure detection and information rebuilding using Polynomial Chaos Theory

A new strategy applying polynomial chaos theory (PCT) to diagnose sensor failure is presented in this paper. Using PCT and uncertainty of parameters, the original state space model of a system is expanded to PCT domain. Variations of sensor output due to parameter uncertainty are calculated by a PCT estimator and presented as PCT output boundaries based on the probability density function (PDF) of uncertain parameters. Along with PCT boundaries, an algorithm is used to diagnose and declare failed sensors. Meanwhile, outputs of failed sensors are frozen and missing information is rebuilt with best-case estimations from PCT estimator. Effectiveness of this method is also verified with two examples.