Vibration Fault Diagnosis of Helicopter Accessory Gearbox Under Multi-operating Conditions
Aiming at the problems of difficulty in fault feature extraction and low recognition accuracy of heli-copter accessory gearbox under limited variable working conditions,a fault diagnosis method is proposed com-bining variational mode decomposition(VMD)and multi-scale convolution neural network(MCNN).Firstly,the helicopter accessory gearbox is tested on the ground and sampled,and the original signal is preprocessed by filtering and noise reduction.Secondly,the VMD decomposition signal is used as several intrinsic mode func-tions(IMF)to reconstruct and normalize the decomposition modes according to the frequency characteristics of the gear meshing ground,so as to enhance the weak high-frequency fault characteristics.Finally,each compo-nent of the reconstructed signal is regarded as a different scale,and multi-scale features are extracted and fused by MCNN.The identified fault category is given by softmax classifier.The test results show that the proposed method can effectively enhance the signal fault characteristics,excavate the difference and identity of signals un-der multiple working conditions.In the vibration fault diagnosis of helicopter accessory gearbox,the average ac-curacy rate is 97.25%.
NETL
NSTL
万方数据
维普
