基于多尺度LSTM预测模型研究

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

摘要/Abstract

摘要：

航空发动机剩余寿命（remaining useful life，RUL）预测是设备故障预测与健康管理（prognostics and health management，PHM）的核心问题。针对发动机数据维度高、滞后性强和复杂度高等挑战，提出了一种基于自训练权重的多尺度注意力双向长短期记忆神经网络模型。通过不同尺度的双向长短期记忆神经网络（bidirectional long short-term memory neural network，BiLSTM）提取多尺度特征；提出一种基于自训练权重的融合算法，通过引入注意力机制进行不同尺度的特征筛选，以提高预测精度。将各模型在NASA的C-MAPSS数据集上进行实验对比，结果证明，所提出预测模型在准确率和均方根误差指标上均有所提升。

关键词: 故障预测与健康管理, 剩余寿命, 双向长短期记忆网络, 自训练权重, 注意力机制, 融合算法

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

中图分类号:

TP391.9

邱俊杰, 郑红, 程云辉. 基于多尺度LSTM预测模型研究[J]. 系统仿真学报, 2022, 34(7): 1593-1604.

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