基于传感器时序信息增强的剩余寿命预测方法

doi:10.7527/S1000-6893.2025.31634

Abstract

Abstract:

To address the challenge of accurately predicting the Remaining Useful Life （RUL） of aircraft engines online， this paper pro-poses a novel RUL prediction method that enhances multi-source sensor temporal feature information. The approach first establishes a prediction network framework by integrating the self-attention mechanism with Bidirectional Long Short-Term Memory （Bi-LSTM） networks. This framework captures the long-term temporal dependencies of multi-source sensor signals and the coupling relationships between their time-varying performance， enabling the extraction of temporal features that in-fluence RUL. To address the potential gradient vanishing issue during training， a residual module is introduced， improving model stability. Additionally， a multi-head self-attention mechanism is employed to extract and enhance key features， leading to dual improvements in both the accuracy and stability of RUL online prediction. Comparative experiments using NASA’s C-MAPSS aircraft engine dataset demonstrate the effectiveness of the proposed method. The results show that the method leverages sensor temporal information to make precise RUL predictions and degradation trend forecasts across a wide range of time and spatial scales. Specifically， the Root Mean Square Error （RMSE） of RUL prediction is reduced by an average of 21.74% compared to other deep learning models， while the coefficient of determination （R²） is improved by an average of 15.81%. This approach offers valuable technical support for the development of aircraft engine health management systems and predictive maintenance strategies.

Key words: aircraft engine, remaining useful life, feature attention, bidirectional long short-term memory network, residual network, multi-head attention mechanism

CLC Number:

V263.6

Guixian QU, Dongyang LIU, Xu YANG, Tian QIU, Chuankai LIU, Shuiting DING, Shuzheng YUAN, Kan GUO. Remaining useful life prediction method based on temporal information enhancement of sensors[J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(17): 231634.

Figures/Tables 18

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传感器序号	参数标号	参数名称	参数单位	传感器序号	参数标号	参数名称	参数单位
1	T2	风机进口总温度	°R	12	phi	燃料流量与P_s30的比率	pps/psi
2	T24	低压压气机出口温度	°R	13	NRf	校正风扇转速	rpm
3	T30	高压压气机出口温度	°R	14	NRc	校正核心转速	rpm
4	T50	低压涡轮出口温度	°R	15	BPR	涵道比
5	P2	风机入口压力	psia	16	farB	燃烧器燃气比
6	P15	旁路管道总压力	psia	17	htBleed	引气焓值
7	P30	高压压气机出口总压力	psia	18	Nf_dmd	设定风扇转速	rpm
8	Nf	发动机风扇速度	rpm	19	PCNfR_dmd	设定核心机换算转速	rpm
9	Nc	发动机核心转速	rpm	20	W31	高压涡轮冷却引气流量	lbm/s
10	epr	发动机压力比		21	W32	低压涡轮冷却引气流量	lbm/s
11	Ps30	高压压气机出口静压	Psia	21	W32	低压涡轮冷却引气流量	lbm/s

训练集	FD001	FD002	FD003	FD004
训练集发动机数量	100	260	100	249
测试集发动机数量	100	259	100	248
操作条件	1	6	1	6
故障模式	1	1	2	2

批次大小	RMSE	R²
4	15.68	0.73
16	16.14	0.72
32	11.27	0.87
64	16.70	0.71
128	19.63	0.60
256	20.61	0.60
512	20.34	0.58

数据集	FD001		FD002		FD003		FD004
数据集	RMSE	R²	RMSE	R²	RMSE	R²	RMSE	R²
LSTM	18.24	0.65	24.81	0.35	17.15	0.66	25.96	0.37
BiLSTM	16.64	0.68	24.57	0.38	16.44	0.69	24.97	0.43
FFBiLSTM	15.43	0.70	23.99	0.41	14.37	0.71	23.26	0.44
FFBiLSTM+MHA	13.47	0.72	21.55	0.52	13.98	0.73	20.17	0.53
FFBiLSTM+ResNet	14.01	0.73	23.71	0.49	14.15	0.75	21.19	0.51
所提方法	11.27	0.87	16.12	0.68	11.52	0.83	15.02	0.71

数据集	FD001		FD002		FD003		FD004
数据集	RMSE	R²	RMSE	R²	RMSE	R²	RMSE	R²
DCNN^［18］	12.61		22.36		12.64		23.31
MODBNE^［19］	15.04		25.05		12.51		28.66
MS-DCNN^［20］	11.44		19.35		11.67		22.22
IDMFFN^［21］	12.18		19.17		11.89		21.72
CNN^［21］	17.91	0.64	22.54	0.42	14.61	0.70	19.85	0.42
CNN-Attention^［22］	16.53	0.69	22.13	0.44	16.43	0.62	18.38	0.50
CNN-LSTM^［22］	14.93	0.75	22.61	0.41	13.15	0.76	20.39	0.39
SARN^［22］	13.84	0.78	18.92	0.59	11.68	0.81	17.74	0.53
所提出的方法	11.27	0.87	16.12	0.68	11.52	0.83	15.02	0.71

Remaining useful life prediction method based on temporal information enhancement of sensors

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