Ferrography Wear Particles Image Recognition Based on Extreme Learning Machine
Joint Authors
Li, Qiong
Zhao, Tingting
Zhang, Lingchao
Sun, Wenhui
Zhao, Xi
Source
Journal of Electrical and Computer Engineering
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-6, 6 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-03-21
Country of Publication
Egypt
No. of Pages
6
Main Subjects
Information Technology and Computer Science
Abstract EN
The morphology of wear particles reflects the complex properties of wear processes involved in particle formation.
Typically, the morphology of wear particles is evaluated qualitatively based on microscopy observations.
This procedure relies upon the experts’ knowledge and, thus, is not always objective and cheap.
With the rapid development of computer image processing technology, neural network based on traditional gradient training algorithm can be used to recognize them.
However, the feedforward neural network based on traditional gradient training algorithms for image segmentation creates many issues, such as needing multiple iterations to converge and easy fall into local minimum, which restrict its development heavily.
Recently, extreme learning machine (ELM) for single-hidden-layer feedforward neural networks (SLFN) has been attracting attentions for its faster learning speed and better generalization performance than those of traditional gradient-based learning algorithms.
In this paper, we propose to employ ELM for ferrography wear particles image recognition.
We extract the shape features, color features, and texture features of five typical kinds of wear particles as the input of the ELM classifier and set five types of wear particles as the output of the ELM classifier.
Therefore, the novel ferrography wear particle classifier is founded based on ELM.
American Psychological Association (APA)
Li, Qiong& Zhao, Tingting& Zhang, Lingchao& Sun, Wenhui& Zhao, Xi. 2017. Ferrography Wear Particles Image Recognition Based on Extreme Learning Machine. Journal of Electrical and Computer Engineering،Vol. 2017, no. 2017, pp.1-6.
https://search.emarefa.net/detail/BIM-1175262
Modern Language Association (MLA)
Li, Qiong…[et al.]. Ferrography Wear Particles Image Recognition Based on Extreme Learning Machine. Journal of Electrical and Computer Engineering No. 2017 (2017), pp.1-6.
https://search.emarefa.net/detail/BIM-1175262
American Medical Association (AMA)
Li, Qiong& Zhao, Tingting& Zhang, Lingchao& Sun, Wenhui& Zhao, Xi. Ferrography Wear Particles Image Recognition Based on Extreme Learning Machine. Journal of Electrical and Computer Engineering. 2017. Vol. 2017, no. 2017, pp.1-6.
https://search.emarefa.net/detail/BIM-1175262
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1175262