考虑随机冲击影响的多部件系统视情维修与备件库存联合优化

doi:10.12305/j.issn.1001-506X.2022.03.20

摘要/Abstract

摘要：

视情维修与备件库存联合优化是确保装备内关键部件安全运行, 降低维修保障成本的有效方法。针对现有模型忽略复杂环境中随机冲击影响的问题, 提出一种考虑随机冲击影响的多部件系统视情维修与备件库存联合优化模型。首先, 建立随机冲击影响下的退化模型及可靠度函数模型, 在首达时间的意义下利用阈值转换思想推导出剩余寿命概率分布, 采用极大似然法估计退化模型参数; 然后, 制定视情维修与备件库存联合策略, 以平均费用率最低为目标建立联合优化模型, 利用粒子群优化算法和蒙特卡罗仿真进行求解; 最后, 通过实例分析和敏感性分析验证了所提模型的有效性和应用价值。

关键词: 视情维修, 备件库存, 联合优化, 随机冲击, 多部件系统

Abstract:

The joint optimization of condition-based maintenance and spare part inventory is an effective method to ensure the safety operation of key components in equipment and reduce maintenance support costs. In order to solve the problem that the existing models ignore the effects of random shock in complex environments, a joint optimization model of condition-based maintenance and spare part inventory for multi-component system considering random shock effects is proposed. Firstly, the degradation model and the reliability model under random shock are established. In the sense of the first hitting time, the probability distribution of the remaining useful life is derived using the idea of threshold conversion, and the degradation model parameters are estimated by the maximum likelihood method. Then, the joint policy of condition-based maintenance and spare part inventory is formulated, and the joint optimization model is established with the target of the lowest average cost ratio. Meanwhile, particle swarm optimization and Monte Carlo simulation are used to solve this model. Finally, the validity and application value of the proposed model are verified through an example analysis and sensitivity analysis.

Key words: condition-based maintenance, spare part inventory, joint optimization, random shock, multi-component system

中图分类号:

李京峰, 陈云翔, 项华春, 王健. 考虑随机冲击影响的多部件系统视情维修与备件库存联合优化[J]. 系统工程与电子技术, 2022, 44(3): 875-883.

Jingfeng LI, Yunxiang CHEN, Huachun XIANG, Jian WANG. Joint optimization of condition-based maintenance and spare part inventory for multi-component system considering random shock effect[J]. Systems Engineering and Electronics, 2022, 44(3): 875-883.

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参数名称	参数值	参数名称	参数值
C_i/元	50	n/个	50
C_f/元	2 000	学习因子c₁	1.494
C_p/元	400	学习因子c₂	1.494
C_d/元	3 000	惯性权重w	0.729
C_o/元	200	N_max/次	100
C_s/元	800	T/d	365
C_h/元	2	N/次	100
t_d/d	30	κ/((°/dGPa)	0.1
D/GPa	2.0	-	-

参数	取值	最优费用率/(元/天)	Δt/d	t_p/d	s/个	S/个
λ	0.50	187.70	6	8	5	21
	1.00	256.05	5	8	6	29
	2.00	399.54	7	8	12	29
μ_W	0.60	183.68	4	8	5	22
	1.21	256.05	5	8	6	29
	2.40	449.01	5	8	11	29
σ_W	0.05	252.66	5	8	6	29
	0.19	256.05	5	8	6	29
	0.80	266.26	5	8	6	29

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