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Fault diagnosis of hydraulic pipeline based on improved time information fusion model

Published:2023-12-26 author: GAO Peng, LI Kaitai, WANG Leilei, et al. Browse: 284 Check PDF documents

Fault diagnosis of hydraulic pipeline based on improved time

information fusion model

GAO Peng1, LI Kaitai2, WANG Leilei1, DOU Hang1, JIANG Junsong3

(1.Department of Intelligent Manufacturing Engineering, Zibo Technician College, Zibo 255000, China; 2.School of

Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China; 3.School of Mechanical

Engineering, Southeast University, Nanjing 210096, China)

Abstract: Aiming at the issues of strong noise interference, low fault recognition rate, and weak model generalization in the fault signals of aviation engine hydraulic pipelines, an improved temporal information fusion model for fault diagnosis of the aero-engine hydraulic pipeline was proposed. Firstly, based on the principle of recurrent neural network, the deformation structure of forward and reverse time information fusion was designed, and the time information fusion model of aviation hydraulic pipeline was constructed, and the LeakyReLU function was introduced to improve the model. Then, the measured one-dimensional aviation pipeline time series data set was input into the improved time information fusion model bidirectional recurrent neural network (Bi-RNN) to update the weight parameters. Finally, based on the same measured data set, the proposed improved temporal information fusion model, long short-term memory neural network (LSTM), recurrent neural network (RNN), support vector machine (SVM) and back propagation neural network (BPNN) were respectively input into five fault diagnosis methods for training. The superiority of the proposed method was verified by comparative analysis. The research results indicate that the improved time information fusion method proposed in this article for hydraulic pipeline fault diagnosis has achieved accurate identification of hydraulic pipeline health status and fault status such as cracks and pits, with an accuracy rate of 99.2%. The overall accuracy and comprehensive index F1-sore have been improved by 5.1%. In terms of comprehensive performance, accuracy, and other indicators, it is significantly superior to other fault diagnosis models, providing a new approach for the diagnosis of hydraulic pipeline faults in aviation engines, and has certain engineering application value.

Key words: hydraulic transmission circuit; time information fusion modal; aerohydraulic pipeline; recurrent neural network(RNN); Leaky ReLU function; weight parameters update

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