Research on Adaptive TSSA-HKRVM Model for Regression Prediction of Crane Load Spectrum

Abstract

For the randomness of crane working load leading to the decrease of load spectrum prediction accuracy with time, an adaptive TSSA-HKRVM model for crane load spectrum regression prediction is proposed. The heterogeneous kernel relevance vector machine model (HKRVM) with comprehensive expression ability is established using the complementary advantages of various kernel functions. The combination strategy consisting of refraction reverse learning, golden sine, and Cauchy mutation + logistic chaotic perturbation is introduced to form a multi-strategy improved sparrow algorithm (TSSA), thus optimizing the relevant parameters of HKRVM. The adaptive updating mechanism of the heterogeneous kernel RVM model under the multi-strategy improved sparrow algorithm (TSSA-HKMRVM) is defined by the sliding window design theory. Based on the sample data of the measured load spectrum, the trained adaptive TSSA-HKRVM model is employed to complete the prediction of the crane equivalent load spectrum. Applying this method to QD20/10 t × 43 m × 12 m general bridge crane, the results show that: compared with other prediction models, although the complexity of the adaptive TSSA-HKRVM model is relatively high, the prediction accuracy of the load spectrum under long periods has been effectively improved, and the completeness of the load information during the whole life cycle is relatively higher, with better applicability.