XU Shi 1 , LUAN Xiao-chi 1 , LI Yan-zheng 1 , SHA Yun-dong 1 , GUO Xiao-peng 2.Inter-shaft Bearing Fault Diagnosis Method Based on LMD and AO-PNN[J].航空发动机,2024,50(2):114-120
Inter-shaft Bearing Fault Diagnosis Method Based on LMD and AO-PNN
Key Words:local mean decomposition  fault diagnosis  correlation-coefficient, energy-ratio, kurtosis criterion  multi-scale permuta⁃ tion entropy  aquila optimizer  inter-shaft bearing  aeroengine
Author NameAffiliation
XU Shi 1 , LUAN Xiao-chi 1 , LI Yan-zheng 1 , SHA Yun-dong 1 , GUO Xiao-peng 2 1. School of Aero-engine Shenyang Aerospace University Shenyang 110136 China 2. AECC Shenyang Engine Research Institute Shenyang 110015 China 
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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.
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