Equipment damage measurement method of wartime based on FCE-PCA-RF

doi:10.23919/JSEE.2024.000065

Journal of Systems Engineering and Electronics ›› 2024, Vol. 35 ›› Issue (3): 707-719.doi: 10.23919/JSEE.2024.000065

• SYSTEMS ENGINEERING • Previous Articles

Equipment damage measurement method of wartime based on FCE-PCA-RF

Mingyu LI¹^,²(), Lu GAO¹^,*(), Hongwei XU¹(), Kai LI¹(), Yisong HUANG¹()

¹ Shijiazhuang Campus of Army Engineering University, Shijiazhuang 050003, China
² Shijiazhuang Campus of Army Infantry Academy, Shijiazhuang 050227, China

Received:2022-03-28 Online:2024-06-18 Published:2024-06-19
Contact: Lu GAO E-mail:1441741393@qq.com;178508362@qq.com;258115205@qq.com;779025078@qq.com;1215459736@qq.com
About author:
LI Mingyu was born in 1993. He received his master’s degree in military equipment and engineering with Army Engineering University, Shijiazhuang, in 2022. His research interests include the theory and application of equipment support. E-mail: 1441741393@qq.com

GAO Lu was born in 1976. He received his doctorate’s degree in military equipment science from the Ordnance Engineering College, Shijiazhuang, in 2006. He is currently an associate professor with Army Engineering University, Shijiazhuang. His research interests include the theory and application of equipment management. E-mail: 178508362@qq.com

XU Hongwei was born in 1987. He received his bachelor’s degree in computer science and technology from the Ordnance Engineering College, Shijiazhuang, in 2010. Now he ispursuing his master’s degree in military equipment and engineering with Army Engineering University, Shijiazhuang. His research interests include the theoryand application of equipment support.E-mail: 258115205@qq.com

LI Kai was born in 1991. He received his bachelor’s degree in road and bridge and sea crossing engineering from National University of Defense Technology, Changsha, in 2016. Now he is pursuing his master’s degree in military equipment and engineering with Army Engineering University, Shijiazhuang. His research interests include the theory and application of equipment support. E-mail: 779025078@qq.com

HUANG Yisong was born in 1993. He received his bachelor’s degree in ground weapon mobile engineering from North University of China, Taiyuan, in 2015. Now he is pursuing his master’s degree in military equipment and engineering in Army Engineering University, Shijiazhuang. His research interests include the theory and application of equipment support. E-mail: 1215459736@qq.com

Abstract

Abstract:

As the “engine” of equipment continuous operation and repeated operation, equipment maintenance support plays a more prominent role in the confrontation of symmetrical combat systems. As the basis and guide for the planning and implementation of equipment maintenance tasks, the equipment damage measurement is an important guarantee for the effective implementation of maintenance support. Firstly,this article comprehensively analyses the influence factors to damage measurement from the enemy’s attributes, our attributes and the battlefield environment starting from the basic problem of wartime equipment damage measurement. Secondly, this article determines the key factors based on fuzzy comprehensive evaluation (FCE) and performed principal component analysis (PCA) on the key factors. Finally, the principal components representing more than 85% of the data features are taken as the input and the equipment damage quantity is taken as the output. The data are trained and tested by artificial neural network (ANN) and random forest (RF). In a word, FCE-PCA-RF can be used as a reference for the research of equipment damage estimation in wartime.

Key words: wartime, equipment damage, fuzzy comprehensive evaluation (FCE), principal component analysis (PCA), artificial neural network (ANN), random forest (RF)

Mingyu LI, Lu GAO, Hongwei XU, Kai LI, Yisong HUANG. Equipment damage measurement method of wartime based on FCE-PCA-RF[J]. Journal of Systems Engineering and Electronics, 2024, 35(3): 707-719.

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Influencing factor	Comprehensive membership			Evaluation result
Influencing factor	High	Middle	Low	Evaluation result
Enemy equipment lethality	0.57	0.2	0.23	High
Application of enemy equipment	0.28	0.265	0.455	Low
Enemy fire strategy	0.505	0.27	0.225	High
Combat style	0.56	0.235	0.205	High
Combat time limit	0.315	0.29	0.395	Low
Annihilation scale	0.4	0.29	0.31	High
Equipment composition	0.59	0.2	0.21	High
Personnel operation quality	0.33	0.2	0.47	Low
Camouflage and protection	0.43	0.3	0.27	High
Natural environment	0.315	0.37	0.315	Middle
Electromagnetic interference	0.295	0.365	0.34	Middle
Cultural environment	0.315	0.35	0.335	Middle

Serial number	Y₁	Y₂	Y₃	Y₄	Y₅	Y₆	Y₇
1	64910	0.07	3.0	0.83	0.83	0.87	0.7
2	82200	0.07	2.4	0.61	0.69	0.68	0.6
3	92200	0.31	2.4	0.44	0.82	0.68	0.4
4	12230	0.59	1.3	0.22	0.11	0.23	0.3
5	22590	0.14	1.5	0.83	0.63	0.77	0.7
6	15980	0.34	1.0	0.67	0.46	0.56	0.6
7	18970	0.34	1.0	0.61	0.47	0.64	0.1
8	14205	0.41	0.6	0.11	0.32	0.41	0.1
9	16970	0.24	1.5	0.44	0.54	0.60	0.6
10	17590	0.62	0.3	0.17	0.12	0.23	0.1
$\vdots $	$\vdots $	$\vdots $	$\vdots $	$\vdots $	$\vdots $	$\vdots $	$\vdots $
21	27900	0.10	2.0	1.00	0.64	0.77	0.6
22	20350	0.21	1.5	0.56	0.52	0.62	0.4
23	32870	0.20	2.0	0.61	0.77	0.82	0.4
24	14450	0.28	0.6	0.67	0.35	0.58	0.6
25	45640	0.31	2.4	0.83	0.75	0.86	0.7
26	22687	0.24	1.4	0.61	0.50	0.62	0.1
27	21890	0.24	1.5	0.61	0.50	0.62	0.9
28	21460	0.21	1.5	0.83	0.63	0.84	0.7
29	25490	0.24	1.6	0.50	0.52	0.64	0.9
30	23480	0.41	0.7	0.28	0.31	0.42	0.1

Main ingredient	Eigenvalue	Contribution rate	Grand total contribution rate
Comp1	4.9905	0.7129	0.7129
Comp2	1.0744	0.1535	0.8664
Comp3	0.4165	0.0595	0.9259
Comp4	0.2424	0.0346	0.9605
Comp5	0.1541	—	—
Comp6	0.1035	—	—
Comp7	0.0187	—	1.00

Variable	Comp1	Comp2
Enemy combat equipment effectiveness index	0.2817	0.6873
Enemy target fire strike effectiveness coefficient	−0.3949	0.1596
Combat style modifier	0.4034	0.2809
Destroy the enemy’s combat power ratio	0.3839	−0.3270
Participation rate of a certain type of equipment	0.4299	0.1792
Camouflage and protection factor	0.4240	−0.0889
Environmental impact factor	0.3001	−0.5256

Model	Data set	R²	MSE
ANN	Train	0.9727	0.0004
ANN	Test	0.8071	0.0014
RF	Train	0.8736	0.0004
RF	Test	0.9127	0.0001

Equipment damage measurement method of wartime based on FCE-PCA-RF

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