Prediction of remaining useful life of aeroengines based on the Transformer with multi-feature fusion

doi:10.3969/j.issn.1000-5641.2022.05.018

Abstract

Abstract:

As the core components of aircraft, engines play a vital role during flight. Accurate prediction of the remaining useful life of the aeroengine can help prognostics and health management, thus preventing major accidents and saving maintenance costs. In view of the lack of consideration of different time steps and the relationship between different sensors and operating conditions in existing methods, a remaining useful life prediction method based on the Transformer was proposed, which fuses multi-feature outputs from different encoder layers. This method selects two input data with different time steps, analyzes the relationship between the sensors using permutation entropy, and extracts features independently from the operating condition data. The experimental results on the public aeroengine dataset CMAPSS (Commercial Modular Aero-Propulsion System Simulation) show that the proposed method is superior to other advanced remaining useful life prediction methods.

Key words: deep learning, remaining useful life, aeroengine, Transformer

CLC Number:

TP399

Yilin MA, Huiling TAO, Qiwen DONG, Ye WANG. Prediction of remaining useful life of aeroengines based on the Transformer with multi-feature fusion[J]. Journal of East China Normal University(Natural Science), 2022, 2022(5): 219-232.

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传感器编号	表示符号	具体描述	单位	传感器编号	表示符号	具体描述	单位
1	T2	风扇入口温度	°R	12	phi	燃料流量与Ps30的比率	pps/psia
2	T24	LPC出口温度	°R	13	NRF	校正后的风扇速率	rpm
3	T30	HPC出口温度	°R	14	NRc	校正后的核心速率	rpm
4	T50	LPT出口温度	°R	15	BPR	涵道比
5	P2	风扇入口压力	psia	16	farB	燃烧室燃料空气比
6	P15	涵道压力	psia	17	htBleed	引气焓值
7	P30	HPC出口压力	psia	18	Nf_dmd	要求的风扇转速	rpm
8	Nf	风扇物理转速	rpm	19	PCNfR_dmd	要求的校正后风扇转速	rpm
9	Nc	核心机物理转速	rpm	20	W31	HPT冷却引气流量	lbm/s
10	epr	发动机压力比率		21	W32	LPT冷却引气流量	lbm/s
11	Ps30	HPC出口静态压力	psia

传感器编号	$ {E_{\rm{entropy}}} $	传感器编号	$ {E_{\rm{entropy}}} $
2	0.6197	12	0.6055
3	0.6275	13	0.6521
4	0.5891	14	0.6060
7	0.5986	15	0.6556
8	0.6482	17	0.6774
9	0.6129	20	0.6566
11	0.5980	21	0.6567

超参数	设置
学习率	0.01
批大小	256
优化器	Adam
输入嵌入层	节点数 64, 激活函数 ReLU
双时间长度编码器层	层数 2, 自注意力头数 4
传感器编码器层	层数 2, 自注意力头数 4
操作条件编码器层	层数 2, 自注意力头数 4
解码器层	层数 2, 自注意力头数 4
解码器前馈全连接层	节点数 64

方法	R_RMSE
方法	数据集1	数据集3
BiLSTM^[18]	13.65	13.74
DBRNN^[19]	17.97	19.18
DCNN^[21]	12.61	12.64
TCN^[22]	11.58	12.67
DARNN^[23]	12.04	10.18
GCU-Transformer^[27]	11.27	11.42
DAST^[28]	11.43	11.32
本文模型	11.04	10.68

方法	S_score
方法	数据集1	数据集3
BiLSTM^[18]	295.00	317.00
DBRNN^[19]	459.89	658.12
DCNN^[21]	273.70	284.10
TCN^[22]	195.10	228.20
DARNN^[23]	261.95	247.85
DAST^[28]	203.15	154.92
本文模型	169.07	195.18