Reliability Approach for Topside Structure of Bucket Wheel Stacker Reclaimer Based on Convolutional Neural Network

doi:10.3901/JME.260067

Abstract

Abstract: Considering the influence of uncertainties on topside structure safety of bucket wheel stacker reclaimer, a reliability approach based on convolutional neural network is presented. Firstly, the uncertainty factors are analyzed in the actual working conditions for the bucket wheel stacker reclaimer and the numerical model of topside structure is established. Secondly, the uncertainty variables in the reliability analysis process of the topside structure are described based on the evidence theory and the reliability analysis model is established. Thirdly, the sample focal elements are collected by using Latin hypercube design and the extreme value is analyzed by using sequential quadratic programming algorithm. Then, the convolutional neural network model is trained to identify the feature attributes of sample focal elements based on the data characteristics and extreme value analysis results. Finally, the trained convolutional neural network model is employed to identify the unknown focal elements, and the reliability confidence intervals of the topside structure under different working conditions are calculated. The results demonstrate that the proposed method could effectively analyze and evaluate the reliability of topside structure of bucket wheel stacker reclaimer, which ensures the topside structure safety. The method can also be used in the field of other balk cargo delivery equipment.

Key words: bucket wheel stacker reclaimer, topside structure, evidence theory, convolutional neural network, reliability analysis

CLC Number:

V224

LIU Xin, LI Feihu, LIU Kai, WAN Jun. Reliability Approach for Topside Structure of Bucket Wheel Stacker Reclaimer Based on Convolutional Neural Network[J]. Journal of Mechanical Engineering, 2026, 62(2): 471-480.

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