基于Self-Attention-LSTM神经网络的设备剩余寿命预测方法

doi:10.1007/s12204-022-2506-6

摘要/Abstract

摘要： 针对机械设备剩余寿命预测中部件间相关性影响考虑不充分的问题，提出一种结合自注意力机制(Self-Attention)与长短期记忆神经网络(LSTM-NN)的剩余寿命预测方法，Self-Attention-LSTM。首先，利用自编码器获取部件级状态信息；其次，将各部件状态信息输入自注意力机制进行部件间关联度学习；然后，多部件关联性矩阵加入LSTM输入门中，运用长短期记忆神经网络进行寿命预测。最后，结合航空发动机系统仿真数据集(C-MAPSS)展开试验并与已有方法进行对比分析，研究结果表明提出方法能够达到较好的预测精度，验证了方法的可行性。

关键词: 设备剩余寿命预测，自注意力机制，长短期记忆神经网络（LSTM-NN），关联性分析

Abstract: : Aiming at the problem of insufficient consideration of the correlation between components in the prediction of the remaining life of mechanical equipment, the method of remaining life prediction that combines the self-attention mechanism with the long short-term memory neural network (LSTM-NN) is proposed, called Self-Attention-LSTM. First, the auto-encoder is used to obtain the component-level state information; second, the state information of each component is input into the self-attention mechanism to learn the correlation between components; then, the multi-component correlation matrix is added to the LSTM input gate, and the LSTM-NN is used for life prediction. Finally, combined with the commercial modular aero-propulsion system simulation data set (C-MAPSS), the experiment was carried out and compared with the existing methods. Research results show that the proposed method can achieve better prediction accuracy and verify the feasibility of the method.

Key words: equipment remaining life prediction, self-attention, long short-term memory neural network (LSTMNN), correlation analysis

中图分类号:

TH17

曹现刚1, 2，雷卓1，李彦川1，张梦园1，段欣宇1. 基于Self-Attention-LSTM神经网络的设备剩余寿命预测方法[J]. J Shanghai Jiaotong Univ Sci, 2023, 28(5): 652-664.

CAO Xiangang1,2(曹现刚)，LEI Zhuol*(雷卓)，LI Yanchuan1(李彦川), ZHANG Mengyuan1(张梦园),DUAN Xinyu1(段欣宇). Prediction Method of Equipment Remaining Life Based on Self-Attention Long Short-Term Memory Neural Network[J]. J Shanghai Jiaotong Univ Sci, 2023, 28(5): 652-664.

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