Research on Prediction of Model Based on Multi-scale LSTM

doi:10.16182/j.issn1004731x.joss.21-0182

Abstract

Abstract:

Aircraft engine remaining useful life (RUL) prediction is the core issue in equipmentfailure prognostics and health management (PHM). Aiming at the characteristics of high dimensionality, high lag and complexity of engine data, a multi-scale attention-based bidirectional long short-term memory neural network model based on self-training weights is proposed. Multi-scale features are extracted through bidirectional long short-term memory neural network (BiLSTM) of different scales. A fusion algorithm based on self-training weights is proposed, and an attention mechanism is introduced to screen features at different scales to improve prediction accuracy. Various models are compared on the NASA's C-MAPSS data set. The results prove that the proposed prediction model improves in both accuracy and root mean square error indicators.

Key words: PHM, RUL, BiLSTM, self-training weights, attention mechanism, fusion algorithm

CLC Number:

TP391.9

Junjie Qiu, Hong Zheng, Yunhui Cheng. Research on Prediction of Model Based on Multi-scale LSTM[J]. Journal of System Simulation, 2022, 34(7): 1593-1604.

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描述	单位
风扇入口总温度	°R
低压压缩机进气道总温度	°R
高压压缩机进气道总温度	°R
低压涡轮进气道总温度	°R
风机进口压力	psia
旁路管道总压	psia
高压压缩机出口处总压力发动机压力比（P50/P2）	psia
物理风机转速	r/min
物理核心速度	r/min
高压压缩机出口静压	r/min
燃料流量与PS30 的比率	pps/psi
修正风机转速	r/min
修正核心转速	r/min
涵道比要求的风扇转速要求的修正风扇转速	rpm r/pm
燃烧器燃气比例
放气热含量
高压涡轮冷却剂释放速度	lbm/s
低压涡轮冷却剂释放速度	lbm/s

参数	取值
激活函数	tanh_shrink
优化器	Adam
初始学习率	0.003
批量尺寸	100
训练周期/个	100

ABiLSTM模型尺度	BiLSTM层数	BiLSTM层节点数
1	1	8
2	2	8，16
3	3	8，16，64
4	4	8，16，64，128

ABiLSTM模型尺度	准确率/%	RMSE	训练时间/s
1	91.61	8.98	264
2	92.51	7.55	488
3	94.47	7.38	664
4	94.40	7.51	889

BiLSTM模型	准确率/%	RMSE	训练时间/s
3	93.15	8.47	639
3+Attenion	94.47	7.38	664
3+STW	94.76	8.07	804