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Fault diagnosis modal of axial piston pump based on prototypical network with small sample size

Published:2023-06-25 author:FAN Jia-qi, LAN Yuan, HUANG Jia-hai, et al. Browse: 504 Check PDF documents

Fault diagnosis modal of axial piston pump based on prototypical

network with small sample size

FAN Jia-qi1, LAN Yuan1,2, HUANG Jia-hai1,2, XIONG Xiao-yan1,2,

LI Guo-yan1,2, LI Li-na1,2

(1.School of Mechanical and Transportation Engineering, Taiyuan University of Technology, Taiyuan 030024,

China; 2.Key Laboratory of New Sensors and Intelligent Control of Ministry of Education, Taiyuan

University of Technology, Taiyuan 030024, China)

Abstract: In practical engineering application, the limited number of fault samples and noise both affect the effect of fault diagnosis of axial piston pump. Therefore, how to improve the performance of fault diagnosis of axial piston pump under the condition of small samples and noise is an urgent problem to be solved.Aiming at the problem that fault diagnosis model based on deep learning tended to be overfitted, and the performance of the model was greatly affected under the condition of limited number of samples and noise, a fault diagnosis model of axial piston pump based on prototypical network with small sample size was proposed.Firstly, a fault diagnosis model of axial piston pump was established, and equal samples of each fault were randomly selected to construct multiple tasks. Onedimensional convolutional neural network was used as the backbone of the model, and each task contains current model, support set and query set.Then, the samples were mapped to the feature space by using the model. In the feature space, the prototype points were constructed by using the similar samples of the support set, and the distance between the sample of the query set and several prototype points was measured one by one, thus realizing the classification of different faults of the axial piston pump.Finally, in order to verify the validity of the fault diagnosis model of axial piston pump based on prototype network, the vibration signals generated by different components of axial piston pump were collected, and the fault identification experiment was carried out by using the above diagnosis model.In order to verify the superiority of this diagnostic model, its performance was compared with that of the model based on convolutional neural network.The experiment results show that the accuracy of the axial piston pump fault diagnosis model based on prototypical network is more than 85% under the condition of limited samples. At the same time, the accuracy under the condition of noise is also more than 85%. The research results show that the performance of model based on prototype network is better than convolutional neural network and traditional methods.

Key words: positive displacement pump; deep learning; limited number of samples; noise resistance; fault classification and identification; diagnostic accuracy rate; visualization of results

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