To improve the validity and reliability of the measured data

primal-dual interior point method was used to reconcile the measured data for a certain type of turbofan engine based on cooperative working equations. The performance of the model on reconciling mass flow rate

temperature and pressure was stidoed bu case when measured data had errors. The physical computation time was also tested using two starting value update methods under various flight conditions. The influence of constraint violation threshold on the precision of data reconciliation was studied. The predictive effect of dynamic data reconciliation on missing parameters was tested

and an approach to make further improvement of computation speed was proposed. Results showed that the uncertainties of measured and unmeasured data could be reduced to less than 1%

meanwhile

the processing time could also be reduced to 5s

providing a support for aero-engine state monitoring

sensor maintenance and generation of aero-engine control strategy in case of sensor failures.