Preventive maintenance strategy for metro vehicle component considering dynamic passenger capacity

doi:10.3785/j.issn.1006-754X.2026.04.157

Chinese Journal of Engineering Design

2026, Vol. 33

Issue (1): 86-94 DOI: 10.3785/j.issn.1006-754X.2026.04.157

Optimization Design

Preventive maintenance strategy for metro vehicle component considering dynamic passenger capacity

Jining GAO(

),Hong WANG(

),Yong HE,Qizhen ZHANG,Hairui QUAN

School of Mechanical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China

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Abstract

Due to the frequent population movement and diverse travel demands within the urban, the metro passenger capacity is unevenly distributed. To explore the complex impacts of the randomness and imbalance of passenger capacity distribution on the economy and the availability of metro vehicle maintenance strategies, a multi-objective optimization method for component maintenance strategies considering dynamic passenger capacity is proposed. Firstly, an accelerated failure model was introduced to develop a component equivalent service life conversion method between random passenger capacity and baseline passenger capacity, leading to the construction of a component failure rate model under the influence of dynamic passenger capacity. Secondly, two maintenance approaches of primary maintenance and advanced maintenance were selected to construct a reliability evolution model for metro vehicle components under different levels of imperfect maintenance. Finally, considering the differentiated train downtime penalty costs caused by uneven passenger capacity distribution and using component maintenance cycles and types as decision variables for maintenance strategies, a maintenance model was established with maintenance cost and maintenance time as optimization objectives, and the optimal maintenance plan was solved using the genetic algorithm. Case study analysis showed that the proposed preventive maintenance strategy could coordinate the train downtime with passenger flow distribution, avoiding high downtime penalty costs caused by the maintenance tasks falling into high passenger flow intervals. Compared with the maintenance strategy that did not consider passenger capacity impact, it could reduce maintenance costs by 9.1% and shorten maintenance time by 4.1%. The research results can provide certain references for improving metro vehicle component maintenance strategies under the influence of relevant factors.

Key words： urban rail transit preventive maintenance genetic algorithm metro vehicle component multi-objective optimization

Received: 15 July 2024 Published: 01 March 2026

CLC:

U 279.3

Corresponding Authors: Hong WANG E-mail: gjnlzjtu@163.com;wh@mail.lzjtu.cn

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Cite this article:

Jining GAO,Hong WANG,Yong HE,Qizhen ZHANG,Hairui QUAN. Preventive maintenance strategy for metro vehicle component considering dynamic passenger capacity. Chinese Journal of Engineering Design, 2026, 33(1): 86-94.

URL:

https://www.zjujournals.com/gcsjxb/10.3785/j.issn.1006-754X.2026.04.157 OR https://www.zjujournals.com/gcsjxb/Y2026/V33/I1/86

考虑动态客运量的地铁车辆部件预防性维修策略

城市内人口流动频繁、出行需求繁多，导致地铁客运量分布不均。为探究客运量分布的随机性和不均衡性对地铁车辆维修策略经济性及可用性的复杂影响，提出了考虑动态客运量的部件维修策略多目标优化方法。首先，引入加速失效模型构建随机客运量与基础客运量间的部件等效役龄转换方法，建立了动态客运量影响下的部件故障率模型。然后，选用初级维修和高级维修两种维修方式，构建了地铁车辆部件经历不同等级非完美维修的可靠度演化模型。最后，考虑到客运量分布不均衡所导致的差异化列车停机惩罚成本，以部件维修周期、维修方式为维修策略的决策变量，建立了以维修成本和维修时间为优化目标的维修模型，并使用遗传算法求解出最优维修计划。算例分析表明，所提出的预防性维修策略可实现列车停机时刻与客流分布的相协调，避免了维修任务落入高客流区间所导致的高额停机惩罚成本，相较于不考虑客运量影响的维修策略，可节省9.1%的维修成本，缩短4.1%的维修时间。研究结果可为相关因素影响下地铁车辆部件维修策略的改进提供一定参考。

关键词： 城市轨道交通, 预防性维修, 遗传算法, 地铁车辆部件, 多目标优化

Fig.1 Schematic of distribution of different passenger flow intervals and preventive maintenance cycle

Fig.2 Failure rate evolution law of component under two-level imperfect maintenance

Fig.3 Multi-objective genetic algorithm flow

参数	取值	参数	取值
c_j/元	320	t_j/h	0.5
c_h/元	680	t_h/h	1.2
c_r/元	1 620	t_r/h	3.0
c_d/元	1 200	$φ s$ /万人	6 390
c_g/元	1 800	$γ$ /(元/人)	5

Table 1 Maintenance parameters of door mechanism

Fig.4 Variation curves of passenger capacity and passenger capacity adjustment factor

Fig.5 Comparison of optimal solution sets under different iterations

优化倾向	维修周期/d	维修时机对应客运区间的 $α k$	维修方式	维修时间 $t a l t$ /h	维修成本 $C$ /元
维修成本 $C$	68	0.87-0.98-1.13-0.97-0.70	0-0-0-0-1	35.01	22 852
维修时间 $t a l t$	48	0.98-0.76-0.98-1.13-1.16-0.97-0.70	0-1-1-1-1-1-1	32.25	24 497

Table 2 Comparison of maintenance plans under different optimization orientations

优化倾向	维修成本 $C$ /元	维修时间 $t a l t$ /h
维修成本 $C$	23 033	35.83
维修时间 $t a l t$	24 664	32.92

Table 3 Optimization results based on multi-objective particle swarm algorithm

方案	维修周期/d	维修时机对应不同客运区间的 $α k$	维修计划	$t a l t$ /h	$C r$ /元	$C p$ /元	$C d$ /元	$C s$ /元	$C$ /元
1	68	0.87-0.98-1.13-0.97-0.70	0-0-0-0-1	35.01	16 200	1 960	4 692	—	22 852
2	46	0.98-0.76-0.98-1.13-1.16-0.97-0.91	0-0-0-0-0-0-1	36.51	13 093	2 600	8 268	1 179	25 140

Table 4 Comparison of two maintenance schemes

$c d$ / （元/人）	方案	$C d$ /元	$C$ /元	C的变化率^①/%
800	1	3 128	21 570	-8.4
800	2	5 512	23 547	-8.4
1 000	1	3 910	22 250	-9.0
1 000	2	6 890	24 460	-9.0
1 200	1	4 692	22 852	-9.1
1 200	2	8 268	25 140	-9.1
1 400	1	5 474	23 424	-8.7
1 400	2	9 646	25 646	-8.7
1 600	1	6 256	23 959	-8.0
1 600	2	11 024	26 046	-8.0

Table 5 Comparison of maintenance costs between scheme 1 and scheme 2 under different values of

c d

$c r$ /元	$t r$ /h	方案	维修时间 $t a l t$ /h	$t a l t$ 变化率^①/%	维修成本 $C$ /元	$C$ 变化率^①/%
972	1.8	1	23.92	-3.0	15 510	-9.3
972	1.8	2	24.65	-3.0	17 091	-9.3
1 296	2.4	1	29.40	-8.7	19 281	-7.4
1 296	2.4	2	32.20	-8.7	21 169	-7.4
1 620	3.0	1	35.01	-4.1	22 852	-9.1
1 620	3.0	2	36.51	-4.1	25 140	-9.1
1 944	3.6	1	39.96	-2.4	26 289	-8.2
1 944	3.6	2	40.93	-2.4	28 629	-8.2
2 268	4.2	1	44.29	-3.3	29 510	-7.2
2 268	4.2	2	45.78	-3.3	31 791	-7.2

Table 6 Comparison of maintenance time and maintenance cost of scheme 1 and scheme 2 under different values of

c r

and

t r

方案	优化目标	维修时间 $t a l t$ /h	维修成本 $C$ /元
3	$C$	35.82	22 852
4	$t a l t$	32.25	25 462

Table 7 Maintenance time and maintenance cost based on single-objective optimization


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