| Abstract:Aiming at the difficulty of the fault diagnosis of inter-shaft bearings of aeroengine due to large noise interference and
complex transmission paths by traditional methods, a fault diagnosis method of inter-shaft bearings based on Local Mean Decomposition
(LMD) and correlation-coefficient, energy-ratio, kurtosis criterion, combined with the Aquila Optimizer (AO) optimized Probabilistic
Neural Network (PNN) was proposed. The vibration signals acquired by the sensors are decomposed by using LMD; The PF components
obtained from the decomposition are screened and determined using the correlation-coefficient, energy-ratio, kurtosis criterion, and the
screened signals are reconstructed; The Multiscale Permutation Entropy (MPE) of the reconstructed signals is calculated to construct
feature vectors; By optimizing the smoothing factor of PNN through AO, the optimized neural network is used for the fault diagnosis of inter-
shaft bearings. The analysis results based on the inter-shaft bearing fault test data show that the proposed method can effectively diagnose
the typical faults of inter-shaft bearings of aeroengines with high background noise and complex path interference, compared with the
Particle Swarm Optimized Probabilistic Neural Network (PSO-PNN)method and the traditional PNN method, the diagnostic accuracy is
improved by 3.875% and 8.125%, respectively, with better global convergence and computational robustness. |
