Integrating Casing Vibration and Reconstructed Pressure Signal for Gas Turbine Blade Fault Diagnosis

Blade faults pose significant risks to the reliability and safety of gas turbines. Blade monitoring conditions through casing vibration is feasible, yet the transmission path attenuates blade features in the signal, leading to inaccuracies in fault diagnosis. Considering the effectiveness and directness of internal pressure signals for diagnosing blade fracture faults, we propose a method that utilizes transfer functions to reconstruct pressure signals from casing vibration, despite the fact that the pressure signal cannot be measured directly during gas turbine operation. This article first investigates the changes and correlations between pressure signals and casing vibrations under blade fracture faults through theoretical modeling. Second, we propose a new framework that uses transfer functions to reconstruct pressure signals from vibration signals and combines it with an autoencoder model for blade fault diagnosis. Moreover, a rotor-blade-casing test rig was constructed to validate the proposed method. Experimental results and comparative analysis show that the method of integrating vibration signals and reconstructing pressure signals can improve the accuracy and robustness of blade fault diagnosis. This research lays a theoretical foundation for more reliable real-time monitoring of gas turbine blades.