Remaining useful life prediction of aircraft engines based on cross-dimensional feature fusion

doi:10.3785/j.issn.1008-973X.2025.07.018

Journal of ZheJiang University (Engineering Science)

2025, Vol. 59

Issue (7): 1504-1513 DOI: 10.3785/j.issn.1008-973X.2025.07.018

Remaining useful life prediction of aircraft engines based on cross-dimensional feature fusion

Dongping ZHANG1(

),Dawei WANG1,Shuji HE1,Siliang TANG2,Zhiyong LIU3,4,Zhongqiu LIU5

1. College of Information Engineering, China Jiliang University, Hangzhou 310018, China
2. College of Computer Science and Technology, Zhejiang University, Hangzhou 310058, China
3. Polytechnic Institute of Zhejiang University, Hangzhou 310015, China
4. Supcon Technology Limited Company, Hangzhou 310053, China
5. Miaxis Biometrics Limited Company, Hangzhou 310052, China

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Abstract

To address the issues of low prediction accuracy, weak generalization ability, and insufficient feature extraction in remaining useful life (RUL) prediction under varying conditions for aircraft engines with multi-sensor variable data, a method based on cross-dimensional multi-scale convolutional network (CDMSCN) was proposed. The cross-influence of different sensor signals on RUL was modeled using a channel attention mechanism. Multi-scale gated convolutional layers were constructed to capture potential fault evolution patterns at different time granularities during engine operation. Hidden channel feature information was extracted through a channel-dependent module. By mining and integrating multi-dimensional features in the engine sensor data, the dynamic characterization and prediction of engine RUL were effectively achieved. Experiments on a public dataset of turbofan engines were carried out, and results showed that the proposed method significantly improved the prediction accuracy of RUL based on complex datasets.

Key words： aircraft engine remaining useful life (RUL) channel attention multi-dimensional feature deep learning

Received: 17 May 2024 Published: 25 July 2025

CLC:

TH 17

Fund: 浙江省重点研发计划项目（2024C01028，2024C01108，2022C01144）.

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	Dongping ZHANG
	Dawei WANG
	Shuji HE
	Siliang TANG
	Zhiyong LIU
	Zhongqiu LIU

Cite this article:

Dongping ZHANG,Dawei WANG,Shuji HE,Siliang TANG,Zhiyong LIU,Zhongqiu LIU. Remaining useful life prediction of aircraft engines based on cross-dimensional feature fusion. Journal of ZheJiang University (Engineering Science), 2025, 59(7): 1504-1513.

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https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2025.07.018 OR https://www.zjujournals.com/eng/Y2025/V59/I7/1504

基于跨维度特征融合的航空发动机寿命预测

针对多传感器变量数据下变工况航空发动机剩余使用寿命（RUL）预测精度低、泛化能力弱及无法充分挖掘数据特征信息的问题，提出基于跨维度多尺度卷积网络（CDMSCN）的RUL预测方法. 利用通道注意力机制建模不同传感器信号对RUL的交叉影响；构建多尺度门控卷积层，捕捉发动机操作过程中不同时间粒度的潜在故障演变模式；通过通道依赖模块挖掘隐藏的通道特征信息. 通过挖掘并整合发动机传感器数据中的多维特征，有效实现对发动机RUL的动态表征和预测. 在涡扇发动机的公开数据集上开展实验，结果表明，所提方法能够有效提高基于复杂数据集的RUL预测精度.

关键词： 航空发动机, 剩余使用寿命（RUL）, 通道注意力, 多维特征, 深度学习

Fig.1 Framework of remaining useful life prediction

Fig.2 Time series embedding module

Fig.3 Channel attention module

Fig.4 Multi-scale temporal dependency module

Fig.5 Gating unit

Fig.6 Schematic diagram of aircraft engine

Tab.1 Main parameters of commercial modular aero-propulsion system simulation dataset

Tab.2 Sensor information in commercial modular aero-propulsion system simulation dataset

Fig.7 Piecewise linear degradation function of remaining useful life

Tab.3 Hyperparameters of cross-dimensional multi-scale convolutional network

Fig.8 Impact of time window sizes on prediction method performance

Fig.9 Impact of channel dimension sizes on prediction method performance

Fig.10 Remaining useful life prediction results of proposed method in four sub-datasets

Tab.4 Comparison of evaluation indicators of different prediction methods in four sub-datasets

Tab.5 Cross-dimensional multi-scale convolutional network and its variants

Tab.6 Performance comparison of cross-dimensional multi-scale convolutional network and its variants

Fig.11 Remaining useful life prediction results of cross-dimensional multi-scale convolutional network and its variants

Fig.12 Visualization of attention scores at different stages of degeneration


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