基于贝叶斯融合与仿真的系统剩余寿命预测

doi:10.12305/j.issn.1001-506X.2021.06.31

摘要/Abstract

摘要：

针对复杂设备的系统级剩余寿命预测问题, 提出一种将贝叶斯理论与仿真相结合的融合预测方法。首先, 采用仿真方法将单机级的多源失效信息折合到系统级, 并转化为验前分布。然后, 结合系统级的现场试验数据, 获得多源信息的验后分布, 并通过加权融合确定联合验后分布, 从而预测系统的剩余寿命。最后, 以某系统作为实例进行方法验证。结果表明，所提方法为系统剩余寿命预测提供了一条可行思路, 并能够在工程实践中运用。

关键词: 寿命预测, 贝叶斯理论, 随机仿真, 多源信息

Abstract:

Aiming at the system level remaining useful life (RUL) prediction of complex equipment, a fusion prediction method combining Bayesian theory with simulation is proposed. Firstly, the simulation method is used to convert the multi-source failure information from the single machine level to the system level, and then to the prior distribution. Then, the post test distribution of multi-source information is obtained by combining the field test data at the system level, and the joint post test distribution is determined by weighted fusion, so as to predict the RUL of the system. Finally, a system is taken as an example to verify the method. The results show that the proposed method provides a feasible idea for the system RUL prediction, and can be applied in engineering practice.

Key words: life prediction, Bayesian theory, stochastic simulation, multi-source information

中图分类号:

TP20

宋兆理, 贾祥, 郭波, 程志君. 基于贝叶斯融合与仿真的系统剩余寿命预测[J]. 系统工程与电子技术, 2021, 43(6): 1706-1713.

Zhaoli SONG, Xiang JIA, Bo GUO, Zhijun CHENG. Remaining useful life prediction of system based on Bayesian fusion and simulation[J]. Systems Engineering and Electronics, 2021, 43(6): 1706-1713.

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寿命分布参数	参数取值
λ₁	5.7×10^-13
λ₂	3.0×10^-12
λ₃	4.5×10^-10
λ₄	1.5×10^-10
λ₅	1.4×10^-15
λ₆	2.9×10^-8
β₁	2.4
β₂	2.6
β₃	2
β₄	2
β₅	3

k	本文方法			仿真方法
k	点估计/年	80%置信区间/年	MSE	点估计/年	80%置信区间/年	MSE
1 000	5.40	[2.93, 7.78]	12.86	6.41	[3.59, 9.10]	22.33
2 000	5.78	[3.25, 8.21]	12.73	6.39	[3.58, 9.09]	23.46
5 000	5.98	[3.32, 8.53]	13.17	6.28	[3.56, 8.91]	22.55
10 000	5.87	[3.28, 8.35]	12.36	5.72	[3.17, 8.16]	17.80
20 000	5.99	[3.36, 8.52]	12.57	5.91	[3.31, 8.40]	19.04
50 000	5.81	[3.23, 8.27]	12.14	5.90	[3.29, 8.39]	18.95

参数名称	参数值
λ₁	0.039 5
λ₂	0.049 5
α₃₄	0.716 7
λ_34a	0.072 4
λ_34b	0.108 9
λ_34c	0.112 5
λ₅	0.031 4
α₆	2.687 0
λ₆	1.47×10^-4
v₇	1.778 0
λ₇	0.386 0

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