基于能量-熵特征和改进堆叠降噪自编码器的水轮机空化状态识别方法OA

[Objectives]Aiming at the problem that the acoustic emission(AE)signal induced by the cavitation of a hydraulic turbine is disturbed by background noise and the fault is difficult to identify,a state recognition method based on energy-entropy features and Harris hawks optimization(HHO)algorithm combined with 3-fold cross-validation(3Fold)to optimize stacked denoising auto encoder(SDAE)is proposed.[Methods]First,the variational mode decomposition algorithm is used to decompose the signal,and then a series of intrinsic mode functions are obtained.Second,the energy and entropy features of the two intrinsic mode functions are extracted,which have the largest correlation coefficient with the original signal.A 12-dimensional feature vector is constructed and then the vector is imported into the recognition model.Third,the HHO algorithm combined with 3Fold is used to optimize the hyper-parameters of SDAE.Finally,the optimal parameters obtained by the HHO-3Fold-SDAE algorithm and other algorithms are input into the model for comparison.[Results]Compared with other algorithms,the HHO-3Fold-SDAE algorithm has smaller loss rate and accuracy variance,and higher average accuracy.Compared with SDAE,its test set average accuracy is increased by 6％.Compared with HHO-SDAE,its test set average accuracy is increased by 4％and accuracy variance is decreased by 17％.[Conclusions]The proposed method can be used to classify and recognize AE signals induced by cavitation of hydraulic turbine,and provide reference for the condition monitoring of hydraulic machinery.