摘要
针对大气数据具有时变性强、类型多、故障复杂等问题,提出一种基于多头自注意力机制的故障诊断方法.首先,使用LSTM提取故障数据的时域特征;然后,结合MSA提取不同类型数据间的空间位置特征;最后,利用MLP提升模型泛化能力,给出了故障诊断流程.该方法在不同类型数据间形成故障互判,实现多故障识别,并利用大气数据对该方法有效性进行充分验证.给出了试验设置,采用带k折交叉验证的网格搜索法确定最优模型参数;为验证LSTM-MSA模型的性能,构建MSA-LSTM、LSTM-MSA-P、LSTM-CNN、RNN-MSA共4种深度学习模型进行对比试验;为验证诊断模型能否精确定位故障,使用验证集的预测标签和真实标签构建故障分类混淆矩阵;为进一步验证文中方法的诊断能力,基于t-SNE进行了可视化试验.结果表明,所提出方法的故障识别准确率达96.696%,F1达96.777%,且各类故障的误判率均控制在10%以下,诊断模型具有较高的鲁棒性.
To solve the problems of strong time-varying,multiple types,complex faults in the air data,a fault diagnosis method was proposed based on the multi-head self-attention mechanism.The long short term memory(LSTM)was used to extract the time domain features of the faulty data,and the multi-head self-attention(MSA)was combined to extract the spatial location features between different types of data.The multilayer perceptron(MLP)was used to improve the generalization ability of the model,and the troubleshooting process was given.By the method,the fault mutual judgements between different types of data were obtained to realize multiple fault identification,and the method was fully verified by the atmospheric data.The experimental setup was given,and the grid search method with k-fold crossvalidation was used to determine the optimal model parameters.To verify the performance of LSTM-MSA model,four deep learning models of MSA-LSTM,LSTM-MSA-P,LSTM-CNN and RNN-MSA were constructed for comparison experiments.To verify whether the diagnostic model can pinpoint faults,the fault classification confusion matrix was constructed using the predicted and true labels of the validation set.To further verify the diagnostic capability of the method,the visualization experiments were conducted based on t-SNE.The results show that the fault recognition accuracy of the proposed method is 96.696%with F,of 96.777%,and the misclassification rates of all kinds of faults are controlled below 10%,which illuminates that the diagnosis model has high robustness.
作者
王力
金辉
WANG Li;JIN Hui(Vocational and Technical Institute,Civil Aviation University of China,Tianjin 300300,China;College of Electronic Information and Automation,Civil Aviation University of China,Tianjin 300300,China)
出处
《江苏大学学报（自然科学版）》
CAS
北大核心
2023年第4期444-451,共8页
Journal of Jiangsu University：Natural Science Edition
基金
国家自然科学基金资助项目(U1733119)
中央高校基本科研业务费资助项目(3122017107)。
关键词
大气数据系统
故障诊断
深度学习
故障仿真
长短期记忆
多头自注意力机制
air data system
troubleshooting
deep learning
fault simulation
long and short-term memory
multi-head self-attention mechanism
