基于改进GRU的航空发动机寿命预测自注意力优化算法

郭晓静; 徐晓慧; 郭佳豪

doi:10.13224/j.cnki.jasp.20220984

基于改进GRU的航空发动机寿命预测自注意力优化算法

doi: 10.13224/j.cnki.jasp.20220984

郭晓静^1,,
徐晓慧^2,*, ,,
郭佳豪²

1.
中国民航大学航空工程学院，天津 300300
2.
中国民航大学电子信息与自动化学院，天津 300300

详细信息

作者简介:
郭晓静（1980-），女，副教授，硕士，研究方向为智能检测、图像处理。E-mail：13820869553@139.com

通讯作者:
徐晓慧（1998-），女，硕士生，研究方向为寿命预测。E-mail：17695640817@163.com

中图分类号: V240.2
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- 被引次数: 0
出版历程
- 收稿日期: 2022-12-27
- 网络出版日期: 2024-04-08

Improved GRU-based self-attention optimization algorithm for aero-engine remaining useful life prediction

GUO Xiaojing^1
,,
XU Xiaohui^{2,*
, ,},
GUO Jiahao²

1.
School of Aeronautical Engineering，Civil Aviation University of China，Tianjin 300300，China
2.
College of Electronic Information and Automation，Civil Aviation University of China，Tianjin 300300，China

摘要

摘要:
航空发动机性能参数具有多元高维及时序性，可表征寿命退行，采用常规模型训练易导致梯度消失。因此提出一种改进门控循环单元（gated recurrent unit）的自注意力（self-attention）优化算法，分析数据源域行梯度及列间相关性，扩增寿命强相关列优化特征权重，加速模型收敛，提高预测精度。在发动机寿命预测数据集（C-MAPSS）上实验表明：该算法得到的寿命方均根误差（RMSE）落在区间[10.52，18.91]，超前预测分值（score）落在区间[48.69，204.98]，相比传统方法大幅降低，改善了寿命预测效果，能够为发动机寿命预测和超前维护提供有效解决方案。
- 剩余使用寿命 /
- 预测与健康管理 /
- 门控循环单元 /
- 自注意力机制 /
- 主成分分析
Abstract:
Multivariate, high-dimensional and time-ordered aero-engine performance parameters can characterize life regressions, which are prone to gradient disappearance using conventional model training. A self-attention optimization algorithm was proposed to improve the gated recurrent units (GRU). Row gradients of source domain and inter-column correlations were analyzed. The feature weights were optimized by augmenting the strongly correlated lifetime columns, with its aim to accelerate model convergence and improve prediction accuracy. Experiments on the engine life prediction dataset (C-MAPSS) showed that the root mean square error (RMSE) of life obtained by the algorithm fell in the interval [10.52, 18.91] and the over-prediction index (score) in the interval [48.69, 204.98]. Compared with the traditional method, the effect of life prediction was greatly reduced, and an effective solution was provided for engine life prediction and advanced maintenance.
- remaining useful life (RUL) /
- prediction and health management /
- gated recurrent units (GRU) /
- self-attention mechanism /
- principal component analysis (PCA)

HTML

图 1 涡扇发动机气路结构^[16]

Figure 1. Turbofan engine air path structure^[16]

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图 2 超前预测分值误差函数图^[16]

Figure 2. Score error function diagram^[16]

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图 3 源域目标域映射关系图

Figure 3. Source domain target domain mapping relationship diagram

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图 4 GRU 结构图

Figure 4. GRU structure diagram

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图 5 模型结构图

Figure 5. Model structure diagram

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图 6 合并训练集与寿命相关性分析图

Figure 6. Merged training set and lifetime correlation analysis graph

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图 7 FD001 训练集部分性能参数图

Figure 7. Graph of some performance parameters of FD001 training set

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图 8 修正目标函数分段退化

Figure 8. Modified objective function segmental degradation curve

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图 9 发动机剩余寿命预测模型

Figure 9. Remaining engine life prediction model

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图 10 实验1与实验2的训练收敛效果

Figure 10. Training convergence effect of experiment 1 and experiment 2

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图 11 实验1对 4 组测试样本中任一发动机的预测情况

Figure 11. Prediction of experimental 1 on any engine of 4 groups test samples

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表 1 数据集（FD001～FD004）信息

Table 1. Information of data set （FD001—FD004）

参数	数据集
参数	FD001	FD002	FD003	FD004
训练发动机单元个数	100	260	100	249
测试发动机单元个数	100	259	100	248
运行工况个数	1	6	1	6
故障模式个数	1	1	2	2

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表 2 性能参数信息

Table 2. Performance parameter information

序号	物理描述	单位
1	风扇入口温度	K
2	低压压缩机出口温度	K
3	高压压缩机出口温度	K
4	低压涡轮出口温度	K
5	风扇进口压强	kPa
6	外涵道压强	kPa
7	高压压缩机出口压强	kPa
8	实际风扇转速	r/min
9	实际核心轴速度	r/min
10	发动机压强比
11	高压压缩机出口静压	Pa
13	风扇修正转速	r/min
14	修正转速	r/min
15	涵道比
16	燃烧室油气比
17	抽气焓
18	风扇转速	r/min
19	风扇修正转速	r/min
20	高压涡轮冷气流量	m³/s
21	低压涡轮冷气流量	m³/s

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表 3 两类实验在4组测试样本中的RMSE和超前预测分值结果

Table 3. RMSE and score results for the two types of experiments in the four test groups

测试数据集	实验1 （合并训练结果）		实验2 （分组训练结果）
测试数据集	E_RMSE	V_score	E_RMSE	V_score
FD001	10.52	48.69	12.40	56.22
FD002	14.83	149.26	20.28	458.84
FD003	10.91	49.99	12.97	190.04
FD004	18.91	204.98	22.88	508.60

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表 4 C-MAPSS数据集消融实验结果对比

Table 4. Comparison of ablation experimental results of C-MAPSS dataset

模型	E_RMSE				V_score
模型	FD001	FD002	FD003	FD004	FD001	FD002	FD003	FD004
GRU	20.87	22.13	18.16	22.69	223.24	672.43	284.66	646.18
CNN-GRU	12.52	17.46	14.34	23.31	50.58	157.91	134.44	444.95
本文模型	10.52	14.83	10.91	18.91	48.69	149.26	49.99	204.98

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表 5 C-MAPSS 数据集不同方法下的 RMSE 与超前预测分值结果对比

Table 5. Comparison of RMSE and score results of C-MAPSS dataset under different methods

方法	FD001		FD002		FD003		FD004
方法	E_RMSE	V_score	E_RMSE	V_score	E_RMSE	V_score	E_RMSE	V_score
AutoEncoder^[17]	13. 58	220	19. 59	2650	19. 16	1727	22. 15	2901
Multi-Head CNN-LSTM^[22]	12.19	330	19.93	2880	12.85	401	22.89	6520
TCAN^[19]	11.64	230.22	17.21	2283.52	11.90	2901	19.17	2510.34
本文方法（合并训练）	10.52	48.69	14.83	149.26	10.91	49.99	18.91	204.98

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