Volume 39 Issue 1
Jan.  2024
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FENG Dongyang, JIANG Chunying, LU Mowu, et al. Fault diagnosis of aircraft landing gear hydraulic system based on TSFFCNN-PSO-SVM[J]. Journal of Aerospace Power, 2024, 39(1):20220111 doi: 10.13224/j.cnki.jasp.20220111
Citation: FENG Dongyang, JIANG Chunying, LU Mowu, et al. Fault diagnosis of aircraft landing gear hydraulic system based on TSFFCNN-PSO-SVM[J]. Journal of Aerospace Power, 2024, 39(1):20220111 doi: 10.13224/j.cnki.jasp.20220111

Fault diagnosis of aircraft landing gear hydraulic system based on TSFFCNN-PSO-SVM

doi: 10.13224/j.cnki.jasp.20220111
  • Received Date: 2022-03-04
    Available Online: 2023-03-21
  • In view of the problems of low fault diagnosis accuracy of aircraft landing gear hydraulic system and difficulty in extracting deep fault features, a fault diagnosis model of landing gear hydraulic system based on the combination of two-stream feature fusion convolutional neural network (TSFFCNN) and particle swarm optimization support vector machine (PSO-SVM) was proposed. The diagnosis model took the pressure signal of multiple nodes as input, the 1D convolutional neural network (1DCNN) and 2D convolutional neural network (2DCNN) parallel multi-channel network structures were adopted to adaptively extract deep feature information, and the deep feature information was fused in the fusion layer. The fusion features were classified into faults through the optimized SVM classifier. In order to verify the proposed fault diagnosis model, a typical aircraft landing gear hydraulic system simulation model was built based on AMESim, and several typical fault type data sets were constructed. The diagnostic results based on the simulation data showed that the accuracy of the proposed fault diagnosis algorithm can reach 99.37%, which can effectively realize the fault diagnosis of the landing gear hydraulic system; compared with other intelligent algorithms, the fault diagnosis model based on TSFFCNN-PSO-SVM had better stability and reliability, higher diagnosis accuracy.

     

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