基于选择性维修的装备群动态健康管理决策

doi:10.12305/j.issn.1001-506X.2024.08.23

Abstract

Abstract:

Prognostics and health management (PHM), as an advanced predictive maintenance method, has become a hot research topic of equipment support. To solve the problem that the existed PHM systems cannot yield dynamic and timely results of health management decision-making, a novel selective maintenance (SM) based model was developed to obtain the optimal decisions, including the optimal maintenance strategies, maintenance task assignment, quantity of spare part ordered, optimal equipment task scheduling, etc. In this model, the imperfect maintenance options, multiple resource constraints, such as personnel, time, cost, etc., spare part ordering and task scheduling, were considered simultaneously. Based on the theory of selective maintenance, a dynamic health management decision-making model was established, and an optimal scheme, which includes optimal maintenance strategies, maintenance task scheduling, number of spare spart ordering, and optimal task planning, is obtained. Finally, an illustrative example was presented to verify the effectiveness of the proposed model, and the effect of the maintenance personnel number, spare part number and mission scheduling on decisions were analyzed. The results showed that the proposed model was of great significance for supporting equipment health management practice and improving the maintenance quality and effectiveness.

Key words: prognostics and health management (PHM), health management decision-making, selective maintenance, fleet, dynamic decision-making, remaining useful life prediction

CLC Number:

Wenbin CAO, Weining MA, Xisheng JIA. Dynamic health management decision-making for fleet based on selective maintenance[J]. Systems Engineering and Electronics, 2024, 46(8): 2760-2769.

Figures/Tables 11

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References 30

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装备j	部件i	维修方式l_{j, i}	任务成功概率	维修费用/元	维修时间/天	备件订购交付时间/天
1	1	1	0.90	0	0	-
	1	2	0.99	1 300	0.5	2
	1	3	0.94	500	1.2	-
	2	1	0.95	0	0	-
	2	2	0.99	1 500	1	1.5
	2	3	0.98	700	1.3	-
2	1	1	0.80	0	0	-
	1	2	0.99	1 300	0.5	2
	1	3	0.84	500	1.2	-
	2	1	0.85	0	0	-
	2	2	0.99	1 500	1	1.5
	2	3	0.90	700	1.3	-
3	1	1	0.75	0	0	-
	1	2	0.99	1 300	0.5	2
	1	3	0.80	500	1.2	-
	2	1	0.70	0	0	-
	2	2	0.99	1 500	1	1.5
	2	3	0.75	700	1.3	-
4	1	1	0.60	0	0	-
	1	2	0.99	1 300	0.5	2
	1	3	0.68	500	1.2	-
	2	1	0.70	0	0	-
	2	2	0.99	1 500	1	1.5
	2	3	0.75	700	1.3	-

装备j	部件i	维修方式l_{j, i}	备件订购数量O_{j, 1}	备件需求量	维修费用/元	维修时间/天	维修任务分配h_{q, (j, I)}	任务规划Ta_j	运转费用c_j^o/元
1	1	3	0	0	500	1.2	(1, 0)	1	1 200
1	2	1	0	0	0	0	-	1	1 200
2	1	1	0	0	0	0	-	1	1 200
2	2	1	0	0	0	0	-	1	1 200
3	1	2	0	1	1 300	0.5	(0, 1)	1	1 200
3	2	2	0	1	1 500	1	(0, 1)	1	1 200
4	1	1	0	0	0	0	-	0	0
4	2	1	0	0	0	0	-	0	0
任务成功概率		0.959		总费用/元		6 900	总维修时间/天		1.5

装备j	部件i	维修方式l_{j, i}	备件订购数量O_{j, 1}	备件需求量	维修费用/元	维修时间/天	维修任务分配h_{q, (j, I)}	任务规划Ta_j	运转费用c_j^o/元
1	1	2	1	1	1 300	2.5	(1, 0)	1	1 200
1	2	3	0	0	700	1.3	(1, 0)	1	1 200
2	1	2	0	1	1 300	0.5	(1, 0)	1	1 200
2	2	2	0	1	1 500	1	(0, 1)	1	1 200
3	1	1	0	0	0	0	-	0	0
3	2	1	0	0	0	0	-	0	0
4	1	1	0	0	0	0	-	0	0
4	2	1	0	0	0	0	-	0	0
任务成功概率		0.951		总费用/元		7 200	总维修时间/天		2.5

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Dynamic health management decision-making for fleet based on selective maintenance

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