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Fault diagnosis of electro-hydraulic proportional servo valves based on attention convolutional capsule networks

  • Yuanhao HU ,
  • Yibo SONG ,
  • Jiahui LIU ,
  • Xiaoli ZHAO ,
  • Wenxiang DENG ,
  • Jian HU ,
  • Jianyong YAO
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  • 1.School of Mechanical Engineering,Nanjing University of Science and Technology,Nanjing  210094,China
    2.State Key Laboratory of Mechanical Transmission for Advanced Equipment,Chongqing University,Chongqing  400044,China

Received date: 2024-03-16

  Revised date: 2024-04-09

  Accepted date: 2024-05-11

  Online published: 2024-06-03

Supported by

National Key R&D Program of China(2021YFB2011300);National Natural Science Foundation of China(52205062);Natural Science Foundation of Jiangsu Province(BK20220950);Fundamental Research Funds for the Central Universities(30922010701);China Postdoctoral Science Foundation(2022M711624);Open Foundation of the State Key Laboratory of Fluid Power and Mechatronic Systems(GZKF-202218);the State Key Laboratory of Mechanical Transmissions(SKLMT-MSKFKT-202220);the Open Fund of Intelligent Control Laboratory(ICL-2023-0305)

Abstract

To address the issues of unknown parameters and difficult modeling in current model-based proportional servo valve fault diagnosis methods, we propose the Attention Convolution Capsule Network (ACCN) algorithm for the first time and design a new proportional servo valve fault diagnosis method based on this network. This algorithm first uses convolutional neural networks and an efficient channel attention mechanism to extract and fuse features from the original multi-channel one-dimensional time-domain signals. The fused results are then input into the subsequent capsule network for further feature extraction and fault classification, yielding the corresponding diagnosis results. The effectiveness of the proposed algorithm and diagnostic method was validated on a proportional servo valve fault simulation test bench. The proposed method achieved 100% accuracy on the test set, and the inclusion of the channel attention mechanism and capsule network enhanced the diagnostic performance of the model to varying degrees.

Cite this article

Yuanhao HU , Yibo SONG , Jiahui LIU , Xiaoli ZHAO , Wenxiang DENG , Jian HU , Jianyong YAO . Fault diagnosis of electro-hydraulic proportional servo valves based on attention convolutional capsule networks[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2024 , 45(15) : 630407 -630407 . DOI: 10.7527/S1000-6893.2024.30407

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