兵工自动化2026，Vol.45Issue(3)：15-21,7.DOI:10.7690/bgzdh.2026.03.003
基于IBES-ELM的无人扫雷车故障诊断方法
Fault Diagnosis Method for Unmanned Mine-sweeping Vehicles Based on IBES-ELM
摘要
Abstract
Aiming at the problems of difficulties in fault detection and the lack of maintenance experience for unmanned mine-sweeping vehicles,a novel method is proposed,featuring rapid detection and high diagnostic accuracy.Building upon the extreme learning machine(ELM)algorithm,the bald eagle search(BES)algorithm is optimized by incorporating the Lévy flight strategy and simulated annealing mechanism.The improved bald eagle search algorithm(IBES)is then utilized to optimize the parameters of the extreme learning network.A fault diagnosis model for the power system of unmanned mine-sweeping vehicles is established,based on the extreme learning machine optimized by the improved bald eagle search algorithm.Experimental results indicate that the fault diagnosis accuracy can reach 98.18%,significantly outperforming the pre-improvement model and other methods.This approach holds both theoretical value and practical significance in engineering applications.关键词
故障诊断/无人扫雷车/极限学习机/秃鹰搜索算法/模拟退火算法/Lévy飞行策略Key words
fault diagnosis/unmanned mine-sweeping vehicles/extreme learning machine/bald eagle search algorithm/simulated annealing algorithm/Lévy flight strategy分类
军事科技引用本文复制引用
刘芳,李英顺,郭占男,匡博琪,郭丽楠..基于IBES-ELM的无人扫雷车故障诊断方法[J].兵工自动化,2026,45(3):15-21,7.基金项目
辽宁省科学技术计划项目(22JH1/1040007) （22JH1/1040007）
