关键词: 联合收割机, 装配质量检测, 联合特征提取, 麻雀搜索算法, 变分模态分解, 深度学习

Abstract: 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 factor α are 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 

Key words: combine harvester, assembly quality inspction, joint feature extraction, sparrow search algorithm, variational mode decomposition, deep learning