Abstract
As a common mechanical part, gear is easy to be damaged because of its complex working environment, which can impact the running of the whole transmission device. Thus, it is very important to evaluate the health of gears in time. A gear fault diagnosis method based on multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) and variational modal extraction (VME) is proposed to solve the problem that the periodic fault features of gears are difficult to be completely extracted from signals. Meanwhile, sparrow search algorithm (SSA) is introduced to optimize the initial parameters of VME and MOMEDA. First, SSA serves to hunt for the best α of VME, VME serves to obtain the signal near the gear fault frequency, and then SSA serves to hunt for the best L and T values of MOMEDA, and MOMEDA serves to strengthen the gear impact features. Finally, the gear impact features are extracted by envelope spectrum. Simulation and experiment show that this method can extract gear fault components from noise effectively with good results.
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