基于混合Gamma分布的机载产品可靠性建模

doi:10.12305/j.issn.1001-506X.2023.02.35

Abstract

Abstract:

The reliability test and evaluation method in the development and design stage cannot accurately infer the airborne products reliability in the service environment, and the single life distribution models such as exponential, normal, Weibull, etc. cannot effectively reflect the reliability of airborne products in complex environments. In older to solve these problems, based on the fault data generated in the service process of the carrier aircraft fleet and the characteristic that the finite mixed Gamma distribution can approximate any probability distribution, the optimization models with mean square error and Pearson statistics as optimization objectives are constructed according to different sample sizes. The adaptive weight particle swarm optimization algorithm is used to optimize the mixed branches and distribution parameters, so as to obtain the product reliability model. The research results can better represent the statistical characteristics of product failures, and provide decision-making basis for system reliability evaluation, preventive maintenance cycle optimization, etc.

Key words: fault data, distribution fitting, parameter evaluation, particle swarm, reliability analysis

CLC Number:

Junliang LI, Huayuan ZHU, Zheng WANG, Liming WANG, Xinlei ZHANG. Reliability modeling of airborne products based on mixed Gamma distribution[J]. Systems Engineering and Electronics, 2023, 45(2): 614-620.

Figures/Tables 11

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样本序号	失效时间/h	故障数	删除数	F_n(t_i)
1	1 300	1	4	0.061 4
2	1 692	1	3	0.149 1
3	2 243	1	4	0.236 8
4	2 278	1	3	0.324 6
5	2 832	1	3	0.412 3
6	2 862	1	3	0.500 0
7	2 931	1	4	0.587 7
8	3 212	1	4	0.675 4
9	3 256	1	4	0.763 2
10	3 410	1	4	0.850 9

故障样本时间
序号	时间	序号	时间	序号	时间	序号	时间
1	266	21	227	41	229	61	261
2	237	22	244	42	240	62	232
3	254	23	262	43	247	63	247
4	243	24	245	44	231	64	252
5	256	25	249	45	239	65	242
6	246	26	252	46	231	66	246
7	248	27	251	47	248	67	250
8	250	28	253	48	266	68	253
9	256	29	239	49	222	69	247
10	239	30	238	50	242	70	242
11	238	31	233	51	241	71	250
12	254	32	236	52	244	72	228
13	230	33	233	53	244	73	224
14	251	34	252	54	244	74	237
15	218	35	246	55	253	75	250
16	225	36	265	56	249	76	234
17	230	37	254	57	258	77	254
18	224	38	254	58	234	78	246
19	230	39	232	59	241	79	240
20	228	40	250	60	239	80	239

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Reliability modeling of airborne products based on mixed Gamma distribution

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