基于多模态特征融合的汽轮发电机转子故障诊断OA

In view of the problem of difficulty in fault diagnosis and identification caused by the vibration signal of steam turbine generators being easily disturbed by noise,an intelligent fault diagnosis method based on VMD(Vari-ational Mode Decomposition)and TCN(Temporal Convolutional Network)is proposed.The optimal combination of the number k value of modal components and the penalty factor α of VMD is determined through the arithmetic optimi-zation algorithm.Thus,the generator vibration data is decomposed by the optimized VMD to obtain multiple modal components,and the signal reconstruction is carried out according to the kurtosis criterion.The reconstructed signal is input into the TCN for feature learning,thereby effectively identifying and diagnosing rotor faults.The rotor vibra-tion data before and after the failure of a 600 MW steam turbine generator are tested as samples.The results show that the proposed method has an accuracy rate of 98.13%in identifying generator faults,which is 4 to 8 percentage points higher than that of other intelligent fault diagnosis models.