《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 of planetary gearbox based on multichannel fusion and multi-scale adaptive residual learning

Published:2023-09-20 author:CHEN Qi, CHEN Chang-zheng, AN Wen-jie. Browse: 337 Check PDF documents

Fault diagnosis of planetary gearbox based on multi-channel fusion and

multi-scale adaptive residual learning

CHEN Qi, CHEN Chang-zheng, AN Wen-jie

(School of Mechanical Engineering, Shenyang University of Technology, Shenyang 110870, China)

Abstract: Aiming at the problems of low accuracy of fault diagnosis for the planetary gearbox of wind turbine and its multiple sources of vibration excitation, a method used to diagnosis the detect of the planetary gearbox was proposed, it was based on multi-channel fusion and multi-scale dynamic adaptive residual learning (MC-MSDARL). Firstly, the proposed multi-scale dynamic adaptive convolution neural networks (MSDAC) was used to dynamically adjust the weights of convolution kernels at different scales to adaptively extract the local and global intrinsic features of single channel data. Secondly, in order to improve the learning ability of the model, the method combined MSDAC with residual learning. Finally, MC-MSDAR was used to fuse the multi-scale features of multi-channel data into a feature vector, and then it was inputted to SoftMax layer to achieve the identification and classification of the fault which was in the planetary gearbox. The research results show that the accuracy of fault diagnosis of planetary gearbox based on MC-MSDAR is 97%, which verifies the effectiveness of this method. When the results of MC-MSDAR is compared with the results implemented by other deep learning methods, the proposed MC-MSDAR has a better performance on generalization ability than other deep learning methods.

Key words: fault diagnosis; wind turbine; planetary gearbox; residual learning; multi-scale learning; multi-scale dynamic adaptive convolution neural networks (MSDAC)

Advanced Search

Online Office System

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