基于时间序列挖掘的合成旅装备维修保障能力预测

doi:10.3969/j.issn.1001-506X.2020.04.19

Abstract

Abstract:

In view of the problem that there are too many parameters in the existing prediction method and the accuracy is not high, the time series mining method is used to predict the equipment maintenance support capability of the synthetic brigade in a certain period of time in the future. Firstly, the index system is established and the data of the "equipment cloud" platform is used to calculate the sequence of indicators and equipment maintenance support ability. Then, the segmentation fitting, clustering, symbolic expression and Apriori association mining are applied to the multivariate time series. The autoregressive integrated moving average-support vector regression (ARIMA-SVR) model and back propagation (BP) neural network are used to predict the equipment maintenance support capability of the synthetic brigade. Finally, the proposed method is verified by an example.

Key words: time series mining, synthetic brigade, equipment maintenance support, Apriori algorithm, autoregressive integrated moving average (ARIMA), support vector regression (SVR), prediction

CLC Number:

E919

Xing SONG, Hongli JIA, Qian WANG, Rudong ZHAO. Prediction of equipment maintenance support capability of synthetic brigade based on time series mining[J]. Systems Engineering and Electronics, 2020, 42(4): 878-886.

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一级指标	二级指标	三级指标
装备品质α₁	可用性α₁₁	固有可用度α₁₁₁ 使用可用度α₁₁₂
	维修性α₁₂	维修诊断性α₁₂₁ 维修可达性α₁₂₂
	配套性α₁₃	诊断设备数量α₁₃₁ 维修设备数量α₁₃₂
人力资源α₂	编制结构α₂₁	维修人员在位率α₂₁₁ 指挥人员比例α₂₁₂
	训练水平α₂₂	训练时间α₂₂₁ 训练成绩α₂₂₂
	维修水平α₂₃	修复率α₂₃₁ 平均修复时间α₂₃₂ 维修响应时间α₂₃₃
维修管理α₃	经费使用α₃₁	经费到位率α₃₁₁ 经费利用率α₃₁₂
	信息资源α₃₂	信息采集率α₃₂₁ 技术资料数量α₃₂₂ 信息安全性α₃₂₃
	保障指挥α₃₃	通信能力α₃₃₁ 指挥能力α₃₃₂ 机动能力α₃₃₃
战场环境α₄	自然环境α₄₁	地理条件α₄₁₁ 气候条件α₄₁₂
	人文环境α₄₂	社会化保障力量α₄₂₁ 民社情情况α₄₂₂
	战场建设α₄₃	野外维修设施建设α₄₃₁ 远程技术支援能力α₄₃₂ 野外仓库开设情况α₄₃₃
器材保障α₅	器材平时供应α₅₁	器材配套率α₅₁₁ 器材响应时间α₅₁₂
器材保障α₅	器材战时保障α₅₂	器材储备情况α₅₂₁ 器材保障方案α₅₂₂

时间/周	1	2	3	4	…	51	52
使用可用度	0.989	0.989	0.986	0.985	…	0.997	0.997
维修人员在位率	1.000	1.000	1.000	0.980	…	0.908	0.900
					$ \ddots $		
器材响应时间	1.000	1.000	1.000	1.000	…	1.000	1.000
器材储备情况	0.995	0.995	0.995	0.995	…	0.913	0.913
器材保障方案	0.971	0.971	0.971	0.971	…	0.970	0.970
装备维修保障能力	0.896	0.901	0.906	0.908	…	0.932	0.932

线段序列	k	w
1	-0.278	0.000
2	-0.463	0.000
3	-0.500	0.167
4	-0.500	0.167
5	0.611	0.167
6	0.185	0.333
7	0.556	0.333
8	0.148	0.500
9	-0.815	0.500
10	-1.000	0.500
11	0.907	1.000
12	0.315	0.833

线段序号	聚类	距离
1	2	0.178
2	2	0.088
3	2	0.106
4	2	0.106
5	5	0.087
6	6	0.086
7	5	0.087
8	6	0.086
9	4	0.093
10	4	0.093
11	1	0.000
12	3	0.000

线段序号	符号化表述
1	a1
2	a1
3	a1
4	a1
5	b1
6	b1
7	b1
8	b1
9	a1
10	a1
11	c1
12	b1

Prediction of equipment maintenance support capability of synthetic brigade based on time series mining

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后项	前项	支持度	置信度
a17	a2	11.765	100
a21	a2	11.765	100
a5	a2	11.765	100
			
c2	d6, a16, d9, c25, d3	15.686	75
d9	c21, b8, d22, c25, c2	15.686	75

模型	离群值数	拟合统计量		Ljung-Box(18)
		R方	正态化BIC	统计量	DF	Sig.
ARIMA (1, 1, 0)	0	0.952	-11.325	23.517	17	0.133

时间/周	绝对误差	相对误差/%
43	0.000	0.000
44	0.004	0.408
45	0.007	0.711
46	0.009	0.911
47	0.009	0.910
48	0.012	1.208
49	0.015	1.506
50	0.015	1.505
51	0.015	1.505
52	0.015	1.505

时间/周	ARIMA预测值	ARIMA残差
1	0.989	0
2	0.989	0
3	0.989	-0.003
4	0.984	0.001
		
39	0.944	0.007
40	0.955	-0.002
41	0.954	0.005
42	0.963	0.004

时间/周	ARIMA 预测值	ARIMA 残差
43	0.972	0.000
44	0.976	0.004
45	0.978	0.007
46	0.979	0.009
47	0.98	0.009
48	0.981	0.012
49	0.981	0.015
50	0.982	0.015
51	0.982	0.015
52	0.982	0.015

时间/周	真实值	预测值	绝对误差	相对误差/%
43	0.980	0.972 1	0.007 9	0.806 1
44	0.985	0.979 3	0.005 7	0.578 7
45	0.988	0.984 4	0.003 6	0.364 4
46	0.989	0.988 1	0.000 9	0.091 0
47	0.993	0.991 2	0.001 8	0.181 3
48	0.996	0.994 5	0.001 5	0.150 6
49	0.997	0.995 9	0.001 1	0.110 3
50	0.997	0.997 3	-0.000 3	-0.030 1
51	0.997	0.997 4	-0.000 4	-0.040 1
52	0.980	0.997 5	-0.017 5	-1.785 7

时间/周	使用可用度	维修人员在位率	训练时间	…	机动能力	地理条件
53	0.997 0	0.896 5	1.000 0	…	1.000 0	1.000 0
54	0.997 1	0.890 2	1.000 0	…	1.000 0	1.000 0
55	0.997 1	0.885 3	1.000 0	…	1.000 0	1.000 0
56	0.997 2	0.877 8	1.000 0	…	1.000 0	1.000 0
57	0.997 2	0.874 9	1.000 0	…	1.000 0	1.000 0
58	0.997 2	0.867 4	1.000 0	…	1.000 0	1.000 0
59	0.997 2	0.863 3	1.000 0	…	1.000 0	1.000 0
60	0.997 2	0.855 5	1.000 0	…	1.000 0	1.000 0
61	0.997 3	0.852 3	1.000 0	…	1.000 0	1.000 0
62	0.997 3	0.845 6	1.000 0	…	1.000 0	1.000 0

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