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Book part
Publication date: 2 July 2018

Yujie Chen, Zhifei Mao and Jack Linchuan Qiu

Abstract

Details

Super-Sticky Wechat and Chinese Society
Type: Book
ISBN: 978-1-78743-091-4

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Book part
Publication date: 2 July 2018

Yujie Chen, Zhifei Mao and Jack Linchuan Qiu

Abstract

Details

Super-Sticky Wechat and Chinese Society
Type: Book
ISBN: 978-1-78743-091-4

To view the access options for this content please click here

Abstract

Details

Super-Sticky Wechat and Chinese Society
Type: Book
ISBN: 978-1-78743-091-4

To view the access options for this content please click here
Book part
Publication date: 2 July 2018

Yujie Chen, Zhifei Mao and Jack Linchuan Qiu

Abstract

Details

Super-Sticky Wechat and Chinese Society
Type: Book
ISBN: 978-1-78743-091-4

To view the access options for this content please click here
Book part
Publication date: 2 July 2018

Yujie Chen, Zhifei Mao and Jack Linchuan Qiu

Abstract

Details

Super-Sticky Wechat and Chinese Society
Type: Book
ISBN: 978-1-78743-091-4

To view the access options for this content please click here
Book part
Publication date: 2 July 2018

Yujie Chen, Zhifei Mao and Jack Linchuan Qiu

Abstract

Details

Super-Sticky Wechat and Chinese Society
Type: Book
ISBN: 978-1-78743-091-4

To view the access options for this content please click here

Abstract

Details

Super-Sticky Wechat and Chinese Society
Type: Book
ISBN: 978-1-78743-091-4

To view the access options for this content please click here

Abstract

Details

Super-Sticky Wechat and Chinese Society
Type: Book
ISBN: 978-1-78743-091-4

To view the access options for this content please click here
Book part
Publication date: 2 July 2018

Yujie Chen, Zhifei Mao and Jack Linchuan Qiu

Abstract

Details

Super-Sticky Wechat and Chinese Society
Type: Book
ISBN: 978-1-78743-091-4

To view the access options for this content please click here
Article
Publication date: 6 March 2017

Yujie Cheng, Hang Yuan, Hongmei Liu and Chen Lu

The purpose of this paper is to propose a fault diagnosis method for rolling bearings, in which the fault feature extraction is realized in a two-dimensional domain using…

Abstract

Purpose

The purpose of this paper is to propose a fault diagnosis method for rolling bearings, in which the fault feature extraction is realized in a two-dimensional domain using scale invariant feature transform (SIFT) algorithm. This method is different from those methods extracting fault feature directly from the traditional one-dimensional domain.

Design/methodology/approach

The vibration signal of rolling bearings is first transformed into a two-dimensional image. Then, the SIFT algorithm is applied to the image to extract the scale invariant feature vector which is highly distinctive and insensitive to noises and working condition variation. As the extracted feature vector is high-dimensional, kernel principal component analysis (KPCA) algorithm is utilized to reduce the dimension of the feature vector, and singular value decomposition technique is used to extract the singular values of the reduced feature vector. Finally, these singular values are introduced into a support vector machine (SVM) classifier to realize fault classification.

Findings

The experiment results show a high fault classification accuracy based on the proposed method.

Originality/value

The proposed approach for rolling bearing fault diagnosis based on SIFT-KPCA and SVM is highly effective in the experiment. The practical value in engineering application of this method can be researched in the future.

Details

Engineering Computations, vol. 34 no. 1
Type: Research Article
ISSN: 0264-4401

Keywords

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