Assembly quality inspection method of combine harvester based on improved VMD and LSTM

Aiming at the problems of low assembly accuracy and difficult assembly quality detection of combine harvesters,a method of combine assembly quality detection based on sparrow search algorithm（SSA）,optimized variational mode decomposition（VMD）and long-term and short-term memory neural network（LSTM）is proposed.Firstly,the optimal VMD decomposition modal parameter K and penalty factorαare obtained by using SSA algorithm,then the vibration signal of the combine is decomposed into eigenmode components IMF with different central frequencies,and the joint features of each IMF are extracted to form a feature vector.Finally,the joint feature vector is used as the input of LSTM to realize the classification of different fault features.The analysis results show that the classification accuracy of SSA-VMD-joint feature extraction method is 98.1%,which is 7.1% and 6.1% higher than that of ensemble empirical mode decomposition（EEMD）and fixed parameter VMD,respectively,and which verifies the superiority of this method to the assembly quality detection of combine harvester.