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Fault diagnosis method for pipe bending of hydraulic line based on LSTM neural network model

Published:2022-12-20 author:MENG Qiu-jing, YANG Gang. Browse: 1122 Check PDF documents

Fault diagnosis method for pipe bending of hydraulic

line based on LSTM neural network model

MENG Qiu-jing1, YANG Gang2

(1.Sino-German Institute of Engineering ,Shanghai Technical Institute of Electronics & Information, Shanghai 201411, China;

2.School of Mechatronics and Vehicle Engineering, Chongqing Jiao-tong University, Chongqing 400074, China)

Abstract: Aiming at the problem that low recognition accuracy caused by noise interference in the fault signals of aircraft engine hydraulic pipelines, a fault diagnosis method based on long short-term memory (LSTM) neural network was proposed. Firstly, vibration signals were collected for the hydraulic fault pipeline of aeroengine, and LSTM model was designed and determined according to the characteristics of pipeline signals. Then, the original vibration signal of hydraulic pipeline was added to Gaussian noise through case analysis, and the hydraulic pipeline data set was created, and the time sequence information of hydraulic pipeline data set was fused by the established long and short-term memory neural network model. Finally,the long and short-term memory neural network model was compared with the recurrent neural network (RNN), convolutional neural network (CNN), support vector machine (SVM) and back propagation neural network (BPNN) models to analyze the fault diagnosis results of hydraulic pipelines. The results show that both short-term and long-term memory neural network model on the identification accuracy of the fault line is better than SVM and BPNN traditional shallow neural network model, the antinoise performance is superior to the CNN and RNN diagnosis methods in recent years, explain LSTM neural network fault diagnosis method for aeroengine hydraulic circuit fault diagnosis has the external adaptability and practicability.

Key words: long short-term memory(LSTM) neural network model; fluid-solid coupling vibration characteristics; vibration signal global characteristics; Gaussian noise; health status identification; time information integration

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