《机电工程》杂志社科技期刊核心期刊论文投稿机电一体化机械、仪表电工自动化、计算机浙江大学主办

Introduction

International Standard Serial Number:

ISSN 1001-4551

Sponsor:

Zhejiang University;

Zhejiang Machinery and Electrical Group

Edited by:

Editorial of Journal of Mechanical & Electrical Engineering

Chief Editor:

ZHAO Qun

Vice Chief Editor:

TANG ren-zhong,

LUO Xiang-yang

Tel:

86-571-87041360,87239525

Fax:

86-571-87239571

Add:

No.9 Gaoguannong,Daxue Road,Hangzhou,China

P.C:

310009

E-mail:

meem_contribute@163.com

Fault diagnosis method of planetary gearbox based on BPSO-M1DCNN

Published:2021-12-22 author:GUO Yong-lun, WU Guo-xin, LIU Xiu-li, et al. Browse: 1046 Check PDF documents

Fault diagnosis method of planetary gearbox based on BPSO-M1DCNN

GUO Yong-lun, WU Guo-xin, LIU Xiu-li, XU Xiao-li

(The Ministry of Education Key Laboratory of Modern Measurement and Control Technology,

Beijing Information Science & Technology University, Beijing 100192, China)

Abstract: When convolution neural network (CNN) was used in fault diagnosis of rotating parts, the utilization of multi-scale fault features was limited, and debugging of network layer structure and hyperparameter were time-consuming and laborious. Aiming at the above problems, a BPSO-M1DCNN algorithm was proposed, which was based on discrete binary particle swarm optimization (BPSO) to optimize multi-scale one dimensional convolution neural network(M1DCNN). Firstly, M1DCNN network was initialized to design, BPSO algorithm was used to adaptively adjust the hyperparameters and network structure to build the BPSO-M1DCNN network. The original vibration data were input to the BPSO-M1DCNN network for feature learning and extraction. Finally, the learned fault features were classified and output. The algorithm was applied to the fault diagnosis of planetary gearbox, and it was compared with network models such as BPSOBP neural network, one-dimensional convolutional neural network (1DCNN), and M1DCNN network. The change curve was used to express the accuracy and loss rate of M1DCNN network and BPSO-M1DCNN network. Confusion matrix was used to show the accuracy of all kinds of fault diagnosis. T-SNE algorithm was used to visualize the feature learning process. The results indicate that, the average accuracy of planetary gearbox test set based on BPSO-M1DCNN network is higher than that of BPSO-BP neural network, 1DCNN network and M1DCNN network, and a better fault diagnosis effect is achieved.

Key words: planetary gearbox; fault diagnosis; multi-scale one dimensional convolution neural network(M1DCNN); binary particle swarm optimization(BPSO)

GUO Yong-lun, WU Guo-xin, LIU Xiu-li, et al. Fault diagnosis method of planetary gearbox based on BPSO-M1DCNN［J］.Journal of Mechanical & Electrical Engineering, 2021,38(10):1277-1283.

Advanced Search

Online Office System

Chinese Core Periodicals
Chinese Sci-tech Core Periodicals
SA, INSPEC Indexed
CSA: T Indexed
UPD:Indexed