Equipment Remaining Useful Life Prediction Method Based on Dual Attention and Selective Ensemble

doi:10.16183/j.cnki.jsjtu.2024.279

Abstract

Abstract:

Accurately predicting remaining useful life (RUL) is crucial for ensuring the stable and reliable operation of large complex equipment. To enhance prediction accuracy while improving model robustness and generalization, a novel prediction method based on dual attention temporal convolutional network (DATCN) and particle swarm optimization with selective ensemble (PSOSEN) is proposed. First, the DATCN is employed to explore the internal correlations between multi-category input features and different time steps in monitoring data, enhancing degradation information from both feature and temporal dimensions. Then, the PSOSEN algorithm prunes underperforming base models at various time scales, autonomously deleting underperforming models and generating an optimal subset of models and assigning optimal weights for weighted output predictions. The proposed method is validated on a dataset of aviation turbofan engine degradation, demonstrating a 13.9% improvement in prediction accuracy compared to BiGRU-TSAM.

Key words: remaining useful life (RUL) prediction, ensemble learning, dual attention mechanism, selective ensemble

CLC Number:

TP183
TH17

FAN Yijing, XIA Tangbin, HAN Dongyang, QI Linlong, WANG Hao, XI Lifeng. Equipment Remaining Useful Life Prediction Method Based on Dual Attention and Selective Ensemble[J]. Journal of Shanghai Jiao Tong University, 2026, 60(5): 800-808.

Figures/Tables 12

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物理特性描述	单位	物理特性描述	单位
风扇入口总温度	°R (1 °R =-273 ℃)	风扇转换转速	r/min
低压压缩机温度	°R	核心机转换转速	r/min
高压压缩机温度	°R	抽汽焓	-
旁路管道总压强	psi (1 psi=6 894.76 Pa)	需求风扇转速	r/min
高压压缩机压强	psi	旁通比	-
低压涡轮温度	°R	燃烧室油气比	-
风扇进口压强	psi	需求风扇转换转速	r/min
物理风扇转速	r/min	高压涡轮冷气流量	磅/s (1磅/s=0.45 kg/s)
物理核心机转速	r/min	低压涡轮冷气流量	磅/s
发动机压力比	-	飞行高度	英尺(1英尺=0.30 m)
高压压缩机静压	psi	马赫数	-
燃料流量与静压比	psi	节流器角度	(°)

名称	训练发动机数量	训练样本数	测试发动机数量	测试样本数	运行模式	失效模式
FD001	100	20630	100	13095	1	1
FD003	100	24720	100	16596	1	2

模型	FD001		FD003
模型	e_RMSE	S	e_RMSE	S
DCNN^[4]	12.61	273.7	12.64	284.1
Bi-LSTM^[19]	13.65	295	13.74	317
CNN-LSTM^[5]	16.13	303	17.12	1420.94
BiGRU-TSAM^[20]	12.56	213.35	12.45	232.86
DA-LSTM^[21]	12.62	263	13.34	360
Hybrid-DiffRUL^[22]	12.65	254.69	13.47	357.83
本文方法	10.82	206	11.65	239