Fault Diagnosis for Gas Turbine Rotor Using MOMEDA-VNCMD

Abstract

It is important rotating machinery for gas turbines in aviation, shipbuilding, and other industries. Given the high failure rate of the gas turbine rotor system, fault diagnosis of the rotor system is completely vital. Aiming at the fault diagnosis of the gas turbine rotor, we adopt a method based on Multipoint Optimal Minimum Entropy Deconvolution Adjusted (MOMEDA)—Variational Nonlinear Chirp Mode Decomposition (VNCMD) in this paper. For the gas turbine rotor test rig data, the original data is first analyzed for effective value, the fault signal is extracted, the fault signal is filtered by MOMEDA, the processed filtered signal is subjected to VNCMD decomposition, and the signal is reconstructed according to the magnitude of spectral kurtosis, and passed Envelope analysis to extract fault characteristics. This paper analyzes the data of the gas turbine rotor test bench, and the results show that the proposed method has achieved excellent results in the fault diagnosis of the gas turbine rotor.