模型与数据混合驱动的IGBT寿命预测方法

doi:10.7527/S1000-6893.2024.30173

Abstract

Abstract:

As a key module of aviation inverter， Insulated Gate Bipolar Transistor （IGBT） plays a decisive role in its safety and reliability. Considering the complex operating conditions of aviation inverter and the fact that IGBT is one of the most vulnerable components for failure， this paper analyzes the failure mechanism and key characteristic parameters of IGBT in aviation inverter. Based on this， an IGBT life prediction method is proposed by combing Long Short-Term Memory （LSTM） network with physical analytical model. The relationship is established for IGBT between its state monitoring data and junction temperature， and the cumulative damage of IGBT is obtained from the physical model， so as to achieve the real-time life prediction of IGBT. Finally， the IGBT accelerated aging experimental dataset provided by the NASA PCoE Center is applied to validate the prediction model. The corresponding results show that the LSTM network combined with the cumulative damage model can effectively predict the lifespan of IGBT， thereby contributing to improving the reliability and reducing the daily maintenance cost of aviation inverters.

Key words: aviation inverter, Insulated Gate Bipolar Transistor (IGBT), Long Short-Term Memory (LSTM) network, life prediction, cumulative damage

CLC Number:

V240.2

Guishuang TIAN, Shaoping WANG, Jian SHI, Mo TAO. IGBT life prediction method driven by model and data[J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(15): 630173-630173.

Figures/Tables 19

Fig.1

Fig.2

Table 1

Switching state of two⁃level aerial inverter

模式	$S a b c$	$u a n$	$u b n$	$u c n$	$u a b$	$u b c$	$u c a$
0	000	0	0	0	0	0	0
1	001	$- 13 U d$	$- 13 U d$	$23 U d$	0	$- U d$	$U d$
2	010	$- 13 U d$	$23 U d$	$- 13 U d$	$- U d$	$U d$	0
3	011	$- 13 U d$	$13 U d$	$13 U d$	$- U d$	0	$U d$
4	100	$23 U d$	$- 13 U d$	$- 13 U d$	$U d$	0	$- U d$
5	101	$13 U d$	$- 23 U d$	$13 U d$	$U d$	$- U d$	0
6	110	$13 U d$	$13 U d$	$- 23 U d$	0	$U d$	$- U d$
7	111	0	0	0	0	0	0

Table 1

Fig.3

Fig.4

Table 2

Fig.5

Fig.6

Fig.7

Table 3

Table 4

Fig.8

Fig.9

Fig.10

Fig.11

Fig.12

Fig.13

Table 5

Fig.14

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试验条件	参数设置
开关频率/kHz	1
栅极电压/V	10
占空比/%	40
温度/℃	330
保护温度/℃	345
循环次数	3 291 800 000

序号	参数设置	取值
1	优化次数	1 200
2	最大优化时间/s	2 161
3	最大迭代次数	300
4	隐含层层数	1
5	单层隐含层细胞数	160
6	第1层隐含层细胞数	32
7	第2层隐含层细胞数	64
8	第3层隐含层细胞数	128
9	初始学习率	5×10^-3
10	遗弃率	0.2

序号	Batch size	LSTM网络层数1		LSTM网络层数2		LSTM网络层数3		GRU网络
序号	Batch size	RMSE	训练时长/s	RMSE	训练时长/s	RMSE	训练时长/s	RMSE	训练时长/s
1	10	0.508	688.54	0.682	1 156.56	0.818	2 161.46	0.739	552.17
2	20	0.432	672.99	0.728	1 151.21	0.741	1 856.17	0.504	459.02
3	40	0.448	673.29	0.656	1 142.97	0.825	1 768.68	0.532	464.87
4	60	0.448	672.16	0.658	1 148.09	0.855	1 767.04	0.688	458.50
5	80	0.498	676.75	0.609	1 153.34	0.729	1 858.63	0.614	464.17
6	100	0.636	688.71	0.703	1 136.68	0.839	1 864.22	0.758	466.85
7	120	0.595	692.14	0.652	1 159.75	0.759	1 789.73	0.576	474.51
8	150	0.467	684.09	0.667	1 160.93	0.774	1 689.43	0.607	484.17
9	200	0.452	744.00	0.710	1 279.06	0.717	1 774.78	0.591	562.06
10	300	0.553	807.06	0.687	1 500.94	0.879	1 946.57	0.618	587.62

IGBT编号	T_m/℃	E_a/（10^-19J·mol^-1）	ΔT_j/℃	D/10^-7
1	338.82	0.99	5.70	0.547 0
2	338.87	0.99	5.71	0.539 8
3	338.79	0.99	5.83	0.573 8
4	338.88	0.99	5.87	0.538 6
5	338.84	0.99	5.84	0.553 3
6	338.82	0.99	6.05	0.561 2

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IGBT life prediction method driven by model and data

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