DOI: 10.11799/ce202312020
An IGWO-BPNN-based method for open-pit mine truck failure prediction
ZHANG Jinpeng
LI Lin
LIU Guangwei
GUO Zhiqing
GUO Weiqian
Abstract：To effectively address the truck failure prediction problem in open-pit mines,we proposed an improved grey wolf algorithm-based BP neural network model,which was successfully applied to predict the frequency and duration of truck failures in open-pit mines.Firstly,considering the limitations of the traditional grey wolf algorithm,new nonlinear updating mechanisms and population updating mechanisms based on linear interpolation were introduced,leading to the development of a multi-strategy integrated improved gray wolf optimizer(IGWO).Secondly,IGWO was applied to search for the weights and thresholds of the BP neural network,forming the IGWO-based BP neural network model(IGWO-BPNN).Finally,using the failure data of trucks from the Baorixile open-pit mine,the IGWO-BPNN model was successfully employed in truck failure prediction research.Results demonstrate that under the same experimental conditions,compared to other algorithms,IGWO-BPNN exhibited superior predictive performance and classification accuracy,effectively aiding open-pit mining enterprises in developing scientifically sound truck preventive maintenance plans and providing scientifically valid foundational decision-making data for the construction of intelligent open-pit mining.
Keywords：open-pit coal minetruck maintenancefailure predictiongray wolf optimizerBP neural network
Publication Date：2023-12-20
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：7( 114-120 )