Research on Health Assessment Method of Key Components in Aircraft Main Power System

doi:10.11985/2023.04.021

Abstract

Abstract:

Data-driven method is used to evaluate the health status of the key components in the aircraft main power system. The rectifier diodes in the rotating rectifier and the metal oxide semiconductor field effect transistor(MOSFET) in the voltage regulator are selected as the key components of the aircraft main power system with high failure rate, and their aging characteristics are analyzed and determined. Using the simulation data, the average excitation voltage and the average excitation current of the exciter are selected as the characteristic variables of the power system to represent the health status of the key components of the system. Principal component analysis(PCA) is used to decouple the data of system characteristic variables, and the characteristic parameters which can represent the aging status of two key components are obtained. The Gaussian mixture model is used to establish the health reference model of key components, and the Mahalanobis distance between the system aging model and the health reference model is calculated. The Mahalanobis distance data is processed by K-means clustering analysis to obtain the data training sample set containing the health status level information of key components, and neural network is used to train the data of the sample set to obtain the health status evaluation classification functions of the key components of the system. Monte Carlo simulation is used to obtain a large amount of data to verify the classification function. The matching degree between the original data and the evaluation results of the classification function of different health status level data of key components is more than 90%, which shows the effectiveness of this health assessment method.

Key words: Aircraft main power system, health assessment, principal component analysis, K-means clustering, back propagation neural network

CLC Number:

TM76

LI Xiaoning, GAO Zhaohui, WANG Shuang, TANG Xiao, LI Yizhuo. Research on Health Assessment Method of Key Components in Aircraft Main Power System[J]. Journal of Electrical Engineering, 2023, 18(4): 188-198.

Figures/Tables 15

References 28

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参数	第1阶段分布范围	第2阶段分布范围	第3阶段分布范围	第4阶段分布范围
VF/V	(1.8,0.1)	(2.4,0.1)	(2.9,0.08)	(3.4,0.06)
IR/A	(25μ,0.99)	(75μ,0.33)	(125μ,0.2)	(175μ,0.14)

特征参数	第一阶段	第二阶段	第三阶段
I_ef/A	0.379 4	0.388 6	0.402 3
df_Ief	10.343 3	10.180 0	9.673 3
V_ef/V	4.642 9	4.808 7	4.642 9
df_Vef	54.783 3	53.803 3	48.010 0

仿真组	二极管正向电压/V	二极管反向漏电流/A	MOS管导通电阻/Ω	循环仿真次数
1	(1.8,0.1)	(25μ,0.99)	(0.6,0.5)	30
2			(1.2,0.25)	30
3			(1.8,0.17)	30
4			(2.4,0.12)	30
5	(2.4,0.1)	(75μ,0.33)	(0.6,0.5)	30
6			(1.2,0.25)	30
7			(1.8,0.17)	30
8			(2.4,0.12)	30
9	(2.9,0.08)	(125μ,0.2)	(0.6,0.5)	30
10			(1.2,0.25)	30
11			(1.8,0.17)	30
12			(2.4,0.12)	30
13	(3.4,0.06)	(175μ,0.14)	(0.6,0.5)	30
14			(1.2,0.25)	30
15			(1.8,0.17)	30
16			(2.4,0.12)	30

仿真组	二极管老化阶段	MOS管老化阶段	整流二极管健康等级分类结果
1	优	优	30次“优”
2		良
3		中
4		差
5	良	优	30次“良”
6		良
7		中
8		差
9	中	优	30次“中”
10		良
11		中
12		差
13	差	优	30次“差”
14		良
15		中
16		差

仿真组	二极管老化阶段	MOS管老化阶段	功率MOS管健康等级分类结果
1	优	优	30次“优”
2		良	6次“优”, 22次“良”, 2次“中”
3		中	25次“中”, 5次“差”
4		差	30次为“差”
5	良	优	30次为“优”
6		良	4次“优”, 26次“良”
7		中	1次“良”, 23次“中”, 6次“差”
8		差	30次为“差”
9	中	优	30次“优”
10		良	6次“优”, 23次“良”, 1次“中”
11		中	26次“中”, 4次“差”
12		差	30次为“差”
13	差	优	30次为“优”
14		良	1次“优”, 26次“良”, 3次“中”
15		中	25次“中”, 5次“差”
16		差	30次为“差”