Qiu Ai Jin
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Planetary Gearbox Fault Diagnosis Based on Multiple Feature Extraction and Information Fusion Combined with Deep Learning
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Affiliation of Author(s):自动化学院

Journal:Zhongguo Jixie Gongcheng

Abstract:According to the heavy noises of vibration signals and the difficulty of incipient fault diagnosis for planetary gearboxes using single classifier, a method of planetary gearbox fault diagnosis was proposed based on multiple feature extraction and information fusion combined with deep learning.The multiple excellent stacked denoising autoencoders(SDAEs) were acquired based on multi-objective evolutionary algorithm.Then, multi-response linear regression model was employed to integrate multiple SDAEs for building multi-obiective ensemble stacked denoising autoencoders (MO-ESDAEs), which was used to diagnose faults of planetary gearboxes.The experimental results show that the proposed method may enhance the fault diagnosis accuracy and stability. © 2019, China Mechanical Engineering Magazine Office. All right reserved.

ISSN No.:1004-132X

Translation or Not:no

Date of Publication:2019-02-25

Co-author:Jin, Qi,wang you ren,Jun Wang

Correspondence Author:Jin, Qi,Qiu Ai Jin

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Associate Professor
Supervisor of Master's Candidates

Main positions:硕士生导师

Other Post:江苏省曲艺担任江苏民间表演艺术研究所秘书

Gender:Female

Alma Mater:上海音乐学院

Education Level:上海音乐学院

Degree:Master's Degree in Literature

School/Department:College of Art

Discipline:新闻与传播

Business Address:南京航空航天大学艺术学院

Contact Information:13913970788

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