Mixed-data multi-modelling for fault detection and isolation

摘要

Early recognition/isolation of a faulty behaviour of a dynamic system is the main task of a fault detection and isolation (FDI). FDI methods based on adaptive probabilistic models with multiple modes represent a theoretically well justified way of solution. Their use is severely restricted by an inherent computational complexity. The complexity problem is addressed here by employing an efficient quasi-Bayes estimation algorithm. It Is directly applicable to the mixture of components created as products of factors belonging to the Exponential family. It opens a novel way to deal adaptively with mixed continuous-discrete, dynamically Related data. The presented theory and algorithmization are illustrated by a simple simulation example.