|
|
Multi-fault recognition of air data system based on multi-head self-attention mechanism |
1. Vocational and Technical Institute, Civil Aviation University of China, Tianjin 300300, China; 2. College of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China |
|
|
Abstract 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 cross-validation 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 F1 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.
|
Received: 07 September 2021
|
|
|
|
[1] |
卢海涛,王自力.综合航空电子系统故障诊断与健康管理技术发展[J].电光与控制,2015,22(8):60-65,86.
|
|
LU H T, WANG Z L.Development review of integrated avionics prognostics and health management technology[J]. Electronics Optics and Control,2015,22(8):60-65,86.(in Chinese)
|
[2] |
YAN H M, MU H N, YI X J, et al. Fault diagnosis of wind turbine based on PCA and GSA-SVM[C]∥Proceedings of the 2019 Prognostics and System Health Management Conference. Piscataway: IEEE. 2019:13-17.
|
[3] |
INTURI V, PRATYUSH A S, SABAREESH G R. Detection of local gear tooth defects on a multistage gearbox operating under fluctuating speeds using DWT and EMD analysis[J]. Arabian Journal for Science and Enginee-ring, DOI:10.1007/s13369-021-05807-0.
|
[4] |
CHEN R X, HUANG X, YANG L X, et al. Intelligent fault diagnosis method of planetary gearboxes based on convolution neural network and discrete wavelet transform[J].Computers in Industry, 2019, 106:48-59.
|
[5] |
ZHANG K, SU J P, SUN S A, et al. Compressor fault diagnosis system based on PCA-PSO-LSSVM algorithm[J]. Science Progress, DOI:10.1177/00368504211026110.
|
[6] |
TANG H, YUAN Z X, DAI H L, et al. Fault diagnosis of rolling bearing based on probability box theory and GA-SVM[J]. IEEE Access, DOI:10.1109/ACCESS.2020.3024792.
|
[7] |
YU L, QU J L, GAO F, et al. A novel hierarchical algorithm for bearing fault diagnosis based on stacked LSTM[J]. Shock and Vibration, DOI:10.1155/2019/2756284.
|
[8] |
牛群,刘志永,褚建川,等.基于长短时记忆网络的仿真系统数据故障诊断方法[J].探测与控制学报,2019,41(5):25-29.
|
|
NIU Q, LIU Z Y, CHU J C, et al. Simulation system datas fault diagnosis based on long short-term memory network[J]. Journal of Detection & Control, 2019,41(5):25-29. (in Chinese)
|
[9] |
于忠坤,王俊峰,唐宾徽,等.基于注意力机制和特征融合的网络威胁情报技战术分类研究[J].四川大学学报(自然科学版), DOI:10.19907/j.0490-6756.2022.053003.
|
|
YU Z K, WANG J F, TANG B H, et al. Research on the classification of cyber threat intelligence techniques and tactics based on attention mechanism and feature fusion[J]. Journal of Sichuan University (Natural Science Edition), DOI:10.19907/j.0490-6756.2022.053003. (in Chinese)
|
[10] |
王亚朝,赵伟,徐海洋,等. 基于多阶段注意力机制的多种导航传感器故障识别研究[J].自动化学报, 2021,47(12):2784-2790.
|
|
WANG Y Z, ZHAO W, XU H Y, et al. Multiple navigation sensor fault diagnose research based on multi-stage attention mechanism[J]. Acta Automatica Sinica, 2021, 47(12):2784-2790. (in Chinese)
|
[11] |
程超. 大气数据系统建模及在组合导航中的应用研究[D]. 成都:电子科技大学, 2019.
|
[12] |
文成林,吕菲亚,包哲静,等.基于数据驱动的微小故障诊断方法综述[J].自动化学报,2016,42(9):1285-1299.
|
|
WEN C L, LYU F Y, BAO Z J, et al. A review of data driven-based incipient fault diagnosis[J]. Acta Automatica Sinica, 2016, 42(9): 1285-1299. (in Chinese)
|
[13] |
邱玉祥,蔡艳,陈霖,等. 基于自回归神经网络的多维时间序列分析[J]. 吉林大学学报(理学版), 2022, 60(5): 1143-1152.
|
|
QIU Y X, CAI Y, CHEN L, et al. Multidimensional time series analysis based on autoregressive neural network[J]. Journal of Jilin University (Science Edition), 2022,60(5): 1143-1152. (in Chinese)
|
[14] |
MENG Z, TIAN S W, YU L, et al. Joint extraction of entities and relations based on character graph convolutional network and multi-head self-attention mechanism[J]. Journal of Experimental & Theoretical Artificial Intelligence, DOI: 10.1080/0952813X.2020.1744198.
|
[15] |
孔子迁,邓蕾,汤宝平,等.基于时频融合和注意力机制的深度学习行星齿轮箱故障诊断方法[J].仪器仪表学报,2019,40(6):221-227.
|
|
KONG Z Q, DENG L, TANG B P, et al. Fault diagnosis of planetary gearbox based on deep learning with time-frequency fusion and attention mechanism[J]. Chinese Journal of Scientific Instrument, 2019,40(6):221-227. (in Chinese)
|
|
|
|