Method for life prediction of parallel branching engine based on multi-modal fusion features

doi:10.11772/j.issn.1001-9081.2025010070

Journal of Computer Applications ›› 2026, Vol. 46 ›› Issue (1): 305-313.DOI: 10.11772/j.issn.1001-9081.2025010070

• Frontier and comprehensive applications • Previous Articles Next Articles

Method for life prediction of parallel branching engine based on multi-modal fusion features

Yanan LI¹, Mengyang GUO¹, Guojun DENG², Yunfeng CHEN², Jianji REN¹, Yongliang YUAN³()

^1.School of Software，Henan Polytechnic University，Jiaozuo Henan 454000，China
^2.Henan Cocyber Information and Technology Company Limited，Zhengzhou Henan 450003，China
^3.School of Mechanical and Power Engineering，Henan Polytechnic University，Jiaozuo Henan 454003，China

Received:2025-02-07 Revised:2025-03-29 Accepted:2025-04-02 Online:2026-01-10 Published:2026-01-10
Contact: Yongliang YUAN
About author:LI Yanan， born in 1983， Ph. D.， associate professor. His research interests include big data analysis， privacy computing.
GUO Mengyang， born in 2001， M. S. candidate. His research interests include big data analysis， privacy computing.
DENG Guojun， born in 1985， senior engineer. His research interests include high-performance computing.
CHEN Yunfeng， born in 1983， M. S. His research interests include distributed system， edge computing.
REN Jianji， born in 1982， Ph. D.， associate professor. His research interests include digital twin， industrial big data.
Supported by:
National Natural Science Foundation of China(52404163);Major Science and Technology Project of Henan Province(231100210200);Science and Technology Research Project of Henan Province(252102221003);Outstanding Young Scholars Program of Henan Polytechnic University(J2025-3);“Double-First-Class” Discipline Construction Project in Surveying and Mapping Science and Technology(CCYJ202420);State Key Laboratory Cultivation Base for Gas Geology and Gas Control(WS2024B20)

基于多模态融合特征的并分支发动机寿命预测方法

李亚男¹, 郭梦阳¹, 邓国军², 陈允峰², 任建吉¹, 原永亮³()

^1.河南理工大学软件学院，河南焦作 454000
^2.河南众诚信息科技股份有限公司，郑州 450003
^3.河南理工大学机械与动力工程学院，河南焦作 454003

通讯作者: 原永亮
作者简介:李亚男（1983—），男，河南温县人，副教授，博士， CCF会员，主要研究方向：大数据分析、隐私计算
郭梦阳（2001—），男，河南禹州人，硕士研究生， CCF学生会员，主要研究方向：大数据分析、隐私计算
邓国军（1985—），男，河南安阳人，高级工程师，主要研究方向：高性能计算
陈允峰（1983—），男，江苏盐城人，硕士，主要研究方向：分布式系统、边缘计算
任建吉（1982—），男，河南温县人，副教授，博士， CCF杰出会员，主要研究方向：数字孪生、工业大数据
基金资助:
国家自然科学基金资助项目(52404163);河南省重大科技专项计划项目(231100210200);河南省科技攻关项目(252102221003);河南理工大学杰出青年资助项目(J2025-3);测绘科学与技术“双一流”学科创建项目(CCYJ202420);河南省瓦斯地质与瓦斯治理重点实验室—省部共建国家重点实验室培育基地资助项目(WS2024B20)

Abstract

Abstract:

Aiming at the problems that engine operation data are multi-modal and it is difficult to achieve effective engine life prediction， a parallel branching engine life prediction method was proposed on the basis of multi-modal features integrating potential relationship between images and engine operation time data. Firstly， a sliding window was used to segment the engine operation data， so as to construct sequence samples of engine operation data， and Gramian Angular Field （GAF） was used to convert the constructed sequence samples into images. Then， the sequence samples and images were processed by a Bi-directional Long Short-Term Memory （BiLSTM） network and a Convolutional Neural Network （CNN） to obtain potential relationship features between sensors such as trends and cycles. Finally， Cross-Attention Mechanism （CAM） was introduced to achieve fusion of the two modal features and realize life prediction of the engine. Experimental results on the public C-MAPSS dataset show that the R-squared （R²） of the prediction method is higher than 0.99 and the Root Mean Square Error （RMSE） of the method is less than 1. It can be seen that the method can improve computational efficiency while ensuring the prediction accuracy.

Key words: life prediction, multi-modal fusion, Gramian Angular Field (GAF), Convolutional Neural Network (CNN), Cross-Attention Mechanism (CAM)

摘要：

针对发动机运行数据的多模态以及难以实现有效的发动机寿命预测问题，提出一种融合图像和发动机运行时间数据潜在关系的多模态融合特征并分支发动机寿命预测方法。首先，利用滑动窗口对发动机运行数据进行分割，以构造发动机运行数据的序列样本，并采用格拉姆角场（GAF）将构造的序列样本转化为图像；其次，用序列样本和图像分别通过双向长短期记忆（BiLSTM）网络和卷积神经网络（CNN）获取趋势和周期等传感器之间的潜在关系特征；最后，引入交叉注意力机制（CAM）实现2种模态特征的融合并实现发动机寿命的预测。在公开的C-MAPSS数据集上的实验结果表明，该预测方法的R-squared （R²）高于0.99，而均方根误差（RMSE）在1以内。可见，该方法能在保证预测精度的同时改善计算效率。

关键词: 寿命预测, 多模态融合, 格拉姆角场, 卷积神经网络, 交叉注意力机制

CLC Number:

TP391.9

Yanan LI, Mengyang GUO, Guojun DENG, Yunfeng CHEN, Jianji REN, Yongliang YUAN. Method for life prediction of parallel branching engine based on multi-modal fusion features[J]. Journal of Computer Applications, 2026, 46(1): 305-313.

李亚男, 郭梦阳, 邓国军, 陈允峰, 任建吉, 原永亮. 基于多模态融合特征的并分支发动机寿命预测方法[J]. 《计算机应用》唯一官方网站, 2026, 46(1): 305-313.

Figures/Tables 17

References 26

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数据集	发动机数		操作状态分类序号	故障类别分类序号
数据集	训练集	测试集	操作状态分类序号	故障类别分类序号
FD001	100	100	1	1
FD002	260	259	6	1
FD003	100	100	1	2
FD004	249	248	6	2

数据集	发动机数		操作状态分类序号	故障类别分类序号
数据集	训练集	测试集	操作状态分类序号	故障类别分类序号
FD001	100	100	1	1
FD002	260	259	6	1
FD003	100	100	1	2
FD004	249	248	6	2

数据集	方法	R²	RMSE	Score
FD001	MPCNN-BiLSTM	0.998 7	0.106 5	0.996 5
	CNN	0.987 6	6.346 6	0.995 9
	BiLSTM	0.974 2	9.180 5	0.995 7
FD002	MPCNN-BiLSTM	0.997 6	0.888 6	0.994 3
	CNN	0.979 7	8.841 7	0.992 2
	BiLSTM	0.966 1	11.437 2	0.991 1

数据集	方法	R²	RMSE	Score
FD001	MPCNN-BiLSTM	0.998 7	0.106 5	0.996 5
	CNN	0.987 6	6.346 6	0.995 9
	BiLSTM	0.974 2	9.180 5	0.995 7
FD002	MPCNN-BiLSTM	0.997 6	0.888 6	0.994 3
	CNN	0.979 7	8.841 7	0.992 2
	BiLSTM	0.966 1	11.437 2	0.991 1

数据集	方法	R²	RMSE	Score
FD001	MPCNN-BiLSTM	0.998 7	0.106 5	0.996 5
	CMGRU	0.987 2	6.445 5	0.996 1
	MSTPP	0.989 8	5.757 8	0.996 3
	sLSTM	0.984 8	7.040 2	0.996 0
	GRU	0.976 6	8.737 4	0.995 9
	XGBoost	0.961 8	11.172 7	0.990 8
FD002	MPCNN-BiLSTM	0.997 6	0.888 6	0.994 3
	CMGRU	0.964 2	11.742 6	0.991 3
	MSTPP	0.983 6	7.928 5	0.990 8
	sLSTM	0.981 8	8.357 7	0.990 7
	GRU	0.957 3	12.835 1	0.990 5
	XGBoost	0.953 1	13.445 8	0.989 3

Method for life prediction of parallel branching engine based on multi-modal fusion features

基于多模态融合特征的并分支发动机寿命预测方法

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Figures/Tables 17

References 26

Related Articles 15

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Metrics

数据集	方法	训练时间/s	测试时间/s
FD001	MPCNN-BiLSTM	47.532 2	0.232 5
	CMGRU	49.173 2	0.284 2
	MSTPP	63.257 1	0.372 5
	sLSTM	45.125 7	0.193 5
	GRU	43.132 4	0.175 1
	XGBoost	15.398 2	0.112 4
FD002	MPCNN-BiLSTM	105.147 6	0.523 3
	CMGRU	118.231 1	0.634 2
	MSTPP	126.165 3	0.775 9
	sLSTM	92.776 1	0.452 8
	GRU	88.335 4	0.425 5
	XGBoost	19.882 6	0.173 5

方法	R²	RMSE	Score
MPCNN-BiLSTM	0.977 5	6.720 4	0.993 5
CMGRU	0.967 4	15.307 4	0.987 9
MSTPP	0.975 2	13.374 1	0.991 4
sLSTM	0.963 4	16.221 6	0.985 2
GRU	0.942 6	20.327 5	0.970 5
XGBoost	0.966 3	15.563 1	0.987 2

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