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FFWR-Net: A feature fusion wear particle recognition network for wear particle classification

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Abstract

Wear particles produced by machines in the process of wear carry valuable information including wear mechanism and wear severity. Wear particle classification based on wear particle images provides predictive analysis for wear condition of machines. A novel wear particle recognition network based on feature fusion, FFWR-Net, is proposed in this research paper for wear particle images classification. In FFWR-Net, traditional feature extraction method by image processing technique (i.e. manually feature extracting) and deep learning convolutional neural network method (i.e. automatically feature extracting) is paralleled to extract the features of wear particle image. Then the features obtained by two different methods are fused together for building a wear particle classifier. In order to verify the effectiveness of the proposed classifier, it is compared with the previous convolutional neural network models on the same wear particle dataset. The comparison results show the accuracy and effectiveness of the proposed FFWR-Net classifier is better than the previous models.

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Acknowledgments

This paper is sponsored by the National Study Abroad Fund of China and supported by The National Key Research and Development Program of China (2017YFB1002304) and Fundamental Research Funds for the Central Universities (FRF-GF-20-16B).

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Authors and Affiliations

  1. Department of Computer, School of Computer and Communication Engineering, University of Science and Technology Beijing (USTB), Beijing, 100083, China

    Suli Fan, Taohong Zhang, Xuxu Guo & Aziguli Wulamu

  2. Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing, 100083, China

    Suli Fan, Taohong Zhang & Aziguli Wulamu

Authors
  1. Suli Fan
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  2. Taohong Zhang
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  3. Xuxu Guo
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  4. Aziguli Wulamu
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Corresponding authors

Correspondence to Taohong Zhang or Aziguli Wulamu.

Additional information

Recommended by Editor Hyung Wook Park

Suli Fan is a master candidate from the School of Computer and Communication Engineering, University of Science and Technology Beijing, PR China. Her current research interests include deep learning, computer vision and object detection.

Taohong Zhang received the Ph.D. degree from the Institute of Mechanics, Chinese Academy of Sciences, China, in 2005. Her current position is an Associate Professor at School of Computer and Communication Engineering, University of Science and Technology Beijing, China. Her research areas cover polymer degradation simulation, computer image processing, and object detection.

Xuxu Guo is a master candidate from the School of Computer and Communication Engineering, University of Science and Technology Beijing, PR China. His current research interests include deep learning, computer vision and object detection.

Aziguli Wulamu is Deputy Director of Beijing Key Laboratory of Knowledge Engineering in materials field of University of Science and Technology Beijing. Her research interests are knowledge engineering, knowledge mapping, deep learning and artificial intelligence. It has undertaken more than 30 national 863 projects, National Science and technology support, national key R&D programs and provincial and ministerial level projects in Beijing, and published more than 40 academic papers.

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Fan, S., Zhang, T., Guo, X. et al. FFWR-Net: A feature fusion wear particle recognition network for wear particle classification. J Mech Sci Technol 35, 1699–1710 (2021). https://doi.org/10.1007/s12206-021-0333-6

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  • DOI: https://doi.org/10.1007/s12206-021-0333-6

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