《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

Time-frequency analysis and fault identification of wind turbine gearbox based on U-net and random forest

Published:2023-10-19 author:ZHANG Pin-yang, CHEN Chang-zheng. Browse: 1030 Check PDF documents

Time-frequency analysis and fault identification of wind turbine gearbox

based on U-net and random forest

ZHANG Pin-yang, CHEN Chang-zheng

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

Abstract: For the intelligent fault diagnosis algorithm of wind turbine gearbox (WTG), the isolated fault classification results often faced the question of confidence. In order to provide more information besides diagnosis conclusion while fault classification, based on the vibration time-frequency information, a two-stage framework for vibration time-frequency analysis and fault diagnosis of wind turbine gearbox was proposed. Firstly, at the first stage, the feature areas related to fault in the time frequency image were marked using the U-net model without manually setting the segmentation parameters. Then, the feature extraction method based on region shape feature was utilized to extract valuable information from the analyzed binary images. Finally, at the second stage, using these shape features based on region information, the random forest algorithm was used to complete the automatic fault identification of the WTGs. The proposed two stage pipeline was used to identify the fault conditions of in-service WTGs located in North China to verify its effectiveness. The experimental results show that the F1 score of the analysis algorithm reaches 0.942, while the diagnostic accuracy of the diagnosis algorithm reaches 97.4%. Comparing with existing methods, Unet method shows higher comprehensive diagnosis performance and computational efficiency. The results show that the proposed method can accurately mark the feature patches and feature bands in time frequency images and quickly and effectively diagnose the WTG faults.

Key words: wind turbine gearbox (WTG); fault diagnosis; vibration analysis; gearbox; time-frequency analysis; random forest

Advanced Search

Online Office System

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