Bearing Fault Diagnosis Based on SVD Feature Extraction and Transfer Learning Classification

被引：0

作者：

Shen, Fei ^{[1
]}

Chen, Chao ^{[1
]}

Yan, Ruqiang ^{[1
,2
]}

Gao, Robert X. ^{[2
]}

机构：

[1] Southeast Univ, Sch Instrument Sci & Engn, Nanjing 210096, Jiangsu, Peoples R China

[2] Case Western Reserve Univ, Dept Mech & Aerosp Engn, Cleveland, OH 44106 USA

来源：

2015 PROGNOSTICS AND SYSTEM HEALTH MANAGEMENT CONFERENCE (PHM) | 2015年

关键词：

fault diagnosis; transfer learning; singular value decomposition (SVD); feature extraction; negative transfer;

D O I：

暂无

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

This paper presents a transfer learning-based approach for bearing fault diagnosis, where the transfer strategy is proposed to improve diagnostic performance of the bearings under various operating conditions. The main idea of transfer learning is to utilize selective auxiliary data to assist target data classification, where a weight adjustment between them is involved in the TrAdaBoost algorithm for enhanced diagnostic capability. In addition, negative transfer is avoided through the similarity judgment, thus improving accuracy and relaxing computational load of the presented approach. Experimental comparison between transfer learning and traditional machine learning has verified the superiority of the proposed algorithm for bearing fault diagnosis.

引用

页数：6

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