Considering the poor accuracy and precision of the gas path analysis (GPA) results due to lack of enough measured gas path parameters and the associated uncertainty

a Bayesian network based multi-source diagnostics information fusion mechanism was proposed to improve the performance of the GPA.The Bayesian network based GPA (BN-GPA) was introduced briefly

then an improved BN-GPA was developed to incorporate diagnostics information from multi sources.In the framework based on the diagnostics information inferred from the measured gas path parameters

other diagnostics information was incorporated into the Bayesian network by the fault mode prior probability table.The results of the simulation show that by fusing the multi sources information

the fault mode which cannot be identified by the traditional GPA now can be identified accurately with reduced variance for the estimation of the health parameters; In another case where the standard deviation of the measurement noise is doubled and the deviations of the health parameters of the faulty components are set less than 1%

the estimation results of the health parameters are still relative accurate with the standard deviation of the estimation result less than 0.1%.