Abstract
Thermal barrier coating (TBC) of turbine blades can prevent aeroengine damage resulting from high temperature. TBC exhibits a multilayer complex structure of ceramic and bonding layers. The ceramic layer is dielectric, whereas the bonding layer is conductive. Disabling either layer can endanger aircraft safety. Changes in TBC parameters are indicative of failure. This study proposed a neural-network-based method to inverse the three key parameters of TBC simultaneously based on electromagnetic/capacitive dual-module sensor. Thus, this method can be used for monitoring the status of aeroengines. The experimental results revealed that the inversion error of thickness and permittivity of the ceramic layer and the conductivity of the bonding layer is less than 2%. Therefore, the proposed method can satisfy application requirements.
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