Combine Assembly Quality Detection Based on Multi-Entropy Data Fusion and Optimized LSSVM

To solve the problems related to the complex structures, multiple parts, and imperceptible assembly quality of combines, this paper compares the performance of the empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), complete ensemble empirical mode decomposition (CEEMD), complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and information entropy features in the detection data of combine assembly quality, and proposes a vibration detection method of combine assembly quality detection based on multi-entropy feature fusion and optimized least squares support vector machine. Firstly, the vibration signals of the combine are decomposed by various adaptive algorithms, and intrinsic modal function (IMF) components are obtained. Three entropy features are extracted from the components of the modes. The features are visualized by t-distributed stochastic neighbor embedding (t-SNE), and the performance of these entropy features in the combine assembly quality detection is analyzed. Secondly, a feature extraction method based on information entropy fusion is proposed. The optimized kernel principal component analysis (KPCA) is used to fuse and reduce the dimension of the entropy features, and form the fusion features. Finally, the extracted features are imported into optimization least squares support vector machine (LSSVM) model for training to judge the working state and assembly quality problem type of combine. The results show that the accuracy of using the unfused entropy features is 82.5%, the accuracy of the fused features is 87.9%. After WOA optimization, the accuracy of the final classification reaches 92.1%, which is better than 89.6% by GA and 90.5% by PSO. It shows that the method proposed in this paper can accurately identify combine problems with different conditions and can be applied to combine assembly quality detection.