Abstract:

To address the problems of over-maintenance and delayed maintenance in traditional maintenance management models following the deployment of large-scale high-tech equipment in military forces， which make it difficult to detect equipment faults in a timely manner and seriously affect the generation of equipment combat effectiveness and the accomplishment of military missions， this paper proposes an improved grey GM（1，1） model based on an HHO-PSO hybrid optimization algorithm that integrates Harris Hawks optimization and particle swarm optimization. By synergizing the global exploration capability of HHO and the local feature extraction capability of PSO， the algorithm optimizes parameters and establishes model adaptation using root mean square error （RMSE） as the convergence criterion， thus effectively improving prediction accuracy. The experimental results indicate that the improved model demonstrates good performance in equipment fault prediction， with low errors and high accuracy， and can provide technical support for early fault warning of equipment.

Key words: particle swarm optimization（PSO）, Harris Hawks optimization（HHO）, hybrid optimization algorithm, fault prediction, GM（1，1） model

摘要：

针对大量高技术装备列装部队后，传统的维护管理模式存在维护过度或延迟的问题，难以及时发现装备故障，严重影响装备战斗力生成和部队使命任务的完成，鉴于此，提出基于哈里斯鹰优化算法与粒子群优化算法融合的HHO-PSO混合优化算法改进灰色GM(1,1)模型。该算法通过协同HHO的全局探索能力和PSO的局部特征提取能力进行参数寻优，构建以均方根误差为收敛准则的模型自适应，有效提升预测精度。实验结果表明：改进模型在装备故障预测中表现较好，误差小、准确度高，可为装备早期故障预警提供技术支持。

关键词: 粒子群优化算法, 哈里斯鹰优化算法, 混合优化算法, 故障预测, GM（1，1）模型