ferrography wear particles image recognition based on extreme learning machine

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ID: 225042
2017
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Abstract
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.
Reference Key
li2017journalferrography Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Qiong Li;Tingting Zhao;Lingchao Zhang;Wenhui Sun;Xi Zhao
Journal Molecular diversity
Year 2017
DOI
10.1155/2017/3451358
URL
http://dx.doi.org/10.1155/2017/3451358
Keywords
computer engineering computer hardware

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