Fault Tree is one of the traditional and conventional approaches used in fault diagnosis. By

identifying combinations of faults in a logical framework it’s possible to define the structure

of the fault tree. The same go with Bayesian networks, but the difference of these probabilistic

tools is in their ability to reasoning under uncertainty. Some typical constraints to the

fault diagnosis have been eliminated by the conversion to a Bayesian network. This paper

shows that information processing has become simple and easy through the use of Bayesian

networks. The study presented showed that updating knowledge and exploiting new knowledge

does not complicate calculations. The contribution is the structural approach of faults

diagnosis of turbo compressor qualitatively and quantitatively, the most likely faults are

defined in descending order. The approach presented in this paper has been successfully

applied to turbo compressor, which represent vital equipment in petrochemical plant.