Gearbox Fault Diagnosis based on Improved LMD and BP Neural Network - Lei He; Suqi Liu; Ting Jiang; Zhijie Huang (Jixie chuandong)
Aiming at the problem of the poor working environment and the fault mode is difficult to identify of the gearbox of military armored vehicles， based on the existing methods， the noise assisted analysis， LMD and BP neural network are combined to apply to the fault diagnosis of armored vehicle gearbox. Firstly， the vibration signals under the four typical states of normal gearbox， bearing clearance fault， indentation of outer ring and broken tooth of gear of the gearbox are collected on the self-built test bench. Then， the signal is decomposed by the noise-assisted LMD method， and the energy eigenvalues of the first eight PF components are extracted. The extracted feature values are used as the input of the BP neural network. The fault type of the gearbox is identified based on the output result. The results show that the method can be effectively applied to the fault diagnosis of military armored vehicle gearbox， and the diagnostic accuracy rate is 92.5%. An effective reference way for other special gearbox diagnosis is provided by this study， and it has certain engineering practical value.