Failure Feature Identification of Vibrating Screen Bolts under Multiple Feature Fusion and Optimization Method

Abstract

Strong impacts and vibrations exist in various structures of rice combine harvesters in harvesting, so the bolt connection structure on the harvesters is prone to loosening and failure, which would further affect the service life and working efficiency of the working device and structure. In this paper, based on the vibration signal acquisition experiment on the bolt and connection structure of the vibrating screen on the harvester, failure feature identification is studied. According to the sensitivity analysis results and the primary extraction of the time-frequency feature, most features have limitations on the identification of failure features of vibrating screen bolts. Therefore, based on the establishment of a high-dimensional feature matrix and multivariate fusion feature matrix, the validity of the feature set was verified based on the whale optimization algorithm. And then, based on the SVM method and high-dimensional mapping of the kernel functions, the high-dimensional feature matrix is trained by the LIBSVM classification decision model. The identify success rates of time domain feature matrix A, frequency domain feature matrix B, WOA-VMD energy entropy matrix C, and normalized multivariate fusion feature matrix G are 64.44%, 74.44%, 81.11%, and more than 90%, respectively, which can reflect the applicability of the failure state identification of the normalized multivariate fusion feature matrix. This paper provided a theoretical basis for the identification of a harvester bolt failure feature.