基于多模态Transformer的机电作动器剩余寿命预测

doi:10.12382/bgxb.2022.0581

摘要/Abstract

摘要：

机电作动器在航空航天装备中扮演着重要角色。针对机电作动器剩余寿命预测问题,提出一种基于多模态Transformer模型的机电作动器寿命预测方法。该方法直接使用多通道传感器数据作为输入,综合考虑多模态数据信息,并且不需要人工特征提取等预处理步骤。多模态Transformer模型利用多头自注意力机制从不同的表示子空间中自适应学习全局特征,能够避免传统深度学习方法难以学习全局特征的缺点。利用多模态Transformer的编码器部分并行提取多模态传感器时间序列中不同传感器的特征,并实时直接预测剩余使用寿命。采用由编码器和解码器组成的完整多模态Transformer模型预测机电作动器的关键性能参数,可同时更直观地预测关键寿命参数的退化过程。使用机电作动器全寿命试验数据验证该方法用于寿命预测的有效性。试验结果表明,所提方法能够准确地直接预测剩余寿命,同时预测关键性能参数的寿命退化过程。

关键词: 机电作动器, 寿命预测, 多模态数据, 注意力机制

Abstract:

Electro-mechanical actuators play an important role in next-generation spacecraft. To address the challenge of predicting the remaining useful life of electro-mechanical actuators, a failure prognostic algorithm based on a multi-mode Transformer model is proposed. Multi-channel sensor data are directly used as inputs to the Transformer model without feature extraction as a pre-processing step. The multi-mode Transformer uses multi-head attention to adaptively learn global features from various representation subspaces. The Encoder of the Transformer is utilized to extract features from different sensors across the time series in parallel and predict the remaining useful life directly. Simultaneously, the full Transformer, composed of an Encoder and a Decoder, is used to prognosticate key performance parameters of electro-mechanical actuators. A benchmark dataset is used to validate the effectiveness of the proposed model for electro-mechanical actuator failure prognostication. Experimental results reveal its advantage in accurate prediction and failure prognosis.

Key words: electro-mechanical actuator, failure prognostic, multimode data, attention mechanism

中图分类号:

V474

陈子涵. 基于多模态Transformer的机电作动器剩余寿命预测[J]. 兵工学报, 2023, 44(10): 2920-2931.

CHEN Zihan. Prognosticating Remaining Useful Life of Electro-Mechanical Actuators Using a Multi-mode Transformer Model[J]. Acta Armamentarii, 2023, 44(10): 2920-2931.

图/表 11

图1 原始Transformer模型架构

Fig.1 Architecture of the Transformer model

图2 多模态Transformer模型架构

Fig.2 Architecture of the multi-mode Transformer model

图3 基于颗粒度一致注意力的改进方式

Fig.3 An improved approach based on Granularity Consistent Attention

图4 FLEA试验台

Fig.4 FLEA test bed

图5 -40lbs载荷时作动器X和作动器Y的电机温度

Fig.5 Temperature of motor housing X and Y with -40 lbs load

表1 试验工况

Table 1 Experiment conditions

负载类型	幅值/mm	频率/Hz	负载/lbs	最大速度/(m·s^-1)
正弦	80	0.25	-40	0.04

图6 EMA寿命试验数据

Fig.6 Overview of EMA degradation test data

图7 剩余寿命预测

Fig.7 Prediction of RUL

表2 直接寿命预测性能对比

Table 2 Performance comparison of direct RUL methods

序号	模型	S分数
1	1DCNN	1503.86
2	LSTM	1387.51
3	多模态Transformer	656.69

图8 电机温度预测

Fig.8 Prediction of motor temperature

表3 间接寿命预测性能对比

Table 3 Performance comparison of indirect RUL methods

序号	模型名称	MAPE
1	GPR	6.62
2	LSTM	5.01
3	原始Transformer	2.33
4	多模态Transformer	1.45

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