Ferrography Wear Particles Image Recognition Based on Extreme Learning Machine
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 objec...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2017/3451358 |
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| _version_ | 1832550494940168192 |
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| author | Qiong Li Tingting Zhao Lingchao Zhang Wenhui Sun Xi Zhao |
| author_facet | Qiong Li Tingting Zhao Lingchao Zhang Wenhui Sun Xi Zhao |
| author_sort | Qiong Li |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-779f9977926d4245b362b71482ebbcf5 |
| institution | Kabale University |
| issn | 2090-0147 2090-0155 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-779f9977926d4245b362b71482ebbcf52025-02-03T06:06:40ZengWileyJournal of Electrical and Computer Engineering2090-01472090-01552017-01-01201710.1155/2017/34513583451358Ferrography Wear Particles Image Recognition Based on Extreme Learning MachineQiong Li0Tingting Zhao1Lingchao Zhang2Wenhui Sun3Xi Zhao4College of Computer Science and Information Engineering, Tianjin University of Science and Technology, Tianjin 300457, ChinaCollege of Computer Science and Information Engineering, Tianjin University of Science and Technology, Tianjin 300457, ChinaCollege of Computer Science and Information Engineering, Tianjin University of Science and Technology, Tianjin 300457, ChinaCollege of Computer Science and Information Engineering, Tianjin University of Science and Technology, Tianjin 300457, ChinaCollege of Computer Science and Information Engineering, Tianjin University of Science and Technology, Tianjin 300457, ChinaThe 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.http://dx.doi.org/10.1155/2017/3451358 |
| spellingShingle | Qiong Li Tingting Zhao Lingchao Zhang Wenhui Sun Xi Zhao Ferrography Wear Particles Image Recognition Based on Extreme Learning Machine Journal of Electrical and Computer Engineering |
| title | Ferrography Wear Particles Image Recognition Based on Extreme Learning Machine |
| title_full | Ferrography Wear Particles Image Recognition Based on Extreme Learning Machine |
| title_fullStr | Ferrography Wear Particles Image Recognition Based on Extreme Learning Machine |
| title_full_unstemmed | Ferrography Wear Particles Image Recognition Based on Extreme Learning Machine |
| title_short | Ferrography Wear Particles Image Recognition Based on Extreme Learning Machine |
| title_sort | ferrography wear particles image recognition based on extreme learning machine |
| url | http://dx.doi.org/10.1155/2017/3451358 |
| work_keys_str_mv | AT qiongli ferrographywearparticlesimagerecognitionbasedonextremelearningmachine AT tingtingzhao ferrographywearparticlesimagerecognitionbasedonextremelearningmachine AT lingchaozhang ferrographywearparticlesimagerecognitionbasedonextremelearningmachine AT wenhuisun ferrographywearparticlesimagerecognitionbasedonextremelearningmachine AT xizhao ferrographywearparticlesimagerecognitionbasedonextremelearningmachine |