基于任务的k/n(G)系统舰船备件需求预测

doi:10.12305/j.issn.1001-506X.2021.08.21

Abstract

Abstract:

For warship spares demand forecasting of k/n(G) system with the combination of overhaul after the task and maintenance afterwards, firstly, a spares demand forecasting model of k/n(G) system is established for exponential distribution spares, and the demand characteristics of the spares is given. Secondly, for convenient understanding and calculating, an engineering interpretation and approximate calculation method of spares demand model is given based on the Gamma single spare parts. And the approximate algorithm is verified. Results show that the demand model describes the spares demand of k/n(G) system in a task period well, and the approximate calculation method takes a good precision, and could make a reference for spares configuration of warship equipment.

Key words: warship spares, overhaul after the task, maintenance afterwards, exponential spares, demand forecast

CLC Number:

E911
V125.7

Haitao LIU, Songshi SHAO, Zhihua ZHANG. Task based demand forecasting of warship spares for k/n(G) system[J]. Systems Engineering and Electronics, 2021, 43(8): 2189-2196.

Figures/Tables 8

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References 23

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备件	j
备件	0	1	2	3	4
p′_j	0.670 3	0.282 0	0.044 5	0.002 9	1.78×10^-5

备件	j
备件	0	1	2	3	4
p_j	0.679 9	0.262 3	0.050 6	0.006 5	6.28×10^-4

任务时间/h	备件故障率	λT₀	分布平均误差	备件平均需求量
任务时间/h	备件故障率	λT₀	分布平均误差	真实值	近似值	相对误差
1 000	0.000 05	0.05	0.002	0.195	0.196	0.004
	0.000 10	0.10	0.007	0.381	0.386	0.013
	0.000 50	0.50	0.038	1.624	1.654	0.018
	0.001 00	1.00	0.061	2.585	2.438	0.057
2 000	0.000 01	0.02	5×10^-4	0.079	0.079	7×10^-4
	0.000 10	0.20	0.015	0.728	0.751	0.032
	0.000 50	1.00	0.061	2.585	2.438	0.057
	0.000 75	1.50	0.075	2.628	2.900	0.104
4 000	0.000 01	0.04	0.002	0.157	0.157	0.003
	0.000 10	0.40	0.034	1.347	1.394	0.035
	0.000 25	1.00	0.061	2.585	2.438	0.057
	0.000 50	2.00	0.080	2.032	3.592	0.768

k, n	λT₀	分布平均误差	备件平均需求量
k, n	λT₀	分布平均误差	真实值	近似值	相对误差
k=1, n=3	0.05	0.002	0.146	0.147	0.002
	0.1	0.006	0.286	0.287	0.005
	0.5	0.044	1.202	1.252	0.042
	1	0.056	2.040	2.099	0.029
k=2, n=3	0.05	0.002	0.147	0.152	0.039
	0.1	0.006	0.287	0.304	0.060
	0.5	0.050	1.157	1.160	0.003
	1	0.097	1.443	1.562	0.083
k=1, n=4	0.05	0.002	0.195	0.195	8×10^-5
	0.1	0.005	0.281	0.382	6×10^-4
	0.5	0.030	1.584	1.617	0.021
	1	0.057	2.660	2.722	0.023
k=3, n=4	0.05	0.003	0.196	0.205	0.051
	0.1	0.008	0.382	0.408	0.067
	0.5	0.055	1.359	1.313	0.034
	1	0.067	1.185	1.123	0.052
k=1, n=6	0.05	0.002	0.293	0.293	2×10^-7
	0.1	0.004	0.571	0.571	6×10^-6
	0.5	0.021	2.363	2.372	0.004
	1	0.042	3.860	3.918	0.015
k=2, n=7	0.05	0.002	0.341	0.341	1×10^-6
	0.1	0.004	0.666	0.666	3×10^-5
	0.5	0.021	2.763	2.792	0.010
	1	0.034	4.655	4.533	0.026
k=3, n=7	0.05	0.002	0.341	0.341	6×10^-5
	0.1	0.005	0.666	0.667	6×10^-4
	0.5	0.025	2.799	2.829	0.010 6
	1	0.040 7	4.810 1	4.601	0.043 4
k=4, n=10	0.05	0.001 6	0.487 7	0.488	8×10^-7
	0.1	0.004 0	0.951 6	0.952	3×10^-5
	0.5	0.017 3	3.963 8	3.989	0.006
	1	0.031 5	6.921 5	6.479	0.064

[1]	YANG Fan, WANG Tiening, WU Longtao, YU Shuangshuang. Unstable equipment material demand forecasting based on tasks [J]. Systems Engineering and Electronics, 2019, 41(12): 2796-2801.
[2]	SHAO Songshi, LIU Haitao, ZHANG Zhihua. Reliability estimation for warship spares by fusing multi-source prior information [J]. Systems Engineering and Electronics, 2019, 41(12): 2905-2910.

Task based demand forecasting of warship spares for k/n(G) system

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