BesNet: binocular ferrographic image recognition model based on deep learning technology
Industrial Lubrication and Tribology
ISSN: 0036-8792
Article publication date: 17 July 2023
Issue publication date: 28 July 2023
Abstract
Purpose
Using computer technology to realize ferrographic intelligent fault diagnosis technology is fundamental research to inspect the operation of mechanical equipment. This study aims to effectively improve the technology of deep learning technology in the field of ferrographic image recognition.
Design/methodology/approach
This paper proposes a binocular image classification model to solve ferrographic image classification problems.
Findings
This paper creatively proposes a binocular model (BesNet model). The model presents a more extreme situation. On the one hand, the model is almost unable to identify cutting wear particles. On the other hand, the model can achieve 100% accuracy in identifying Chunky and Nonferrous wear particles. The BesNet model is a bionic model of the human eye, and the used training image is a specially processed parallax image. After combining the MCECNN model, it is changed to BMECNN model, and its classification accuracy has reached the highest level in the industry.
Originality/value
The work presented in this thesis is original, except as acknowledged in the text. The material has not been submitted, either in whole or in part, for a degree at this or any other university. The BesNet model developed in this article is a brand new system for ferrographic image recognition. The BesNet model adopts a method of imitating the eyes to view ferrography images, and its image processing method is also unique. After combining the MCECNN model, it is changed to BMECNN model, and its classification accuracy has reached the highest level in the industry.
Peer review
The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2023-0150/
Keywords
Acknowledgements
This work is supported by the Science and technology plan of Shanghai Municipality, China (Grant Nos. 20DZ2252300).
Declaration of interests statement: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Statement of originality: The work presented in this thesis is original, except as acknowledged in the text. The material has not been submitted, either in whole or in part, for a degree at this or any other university.
Citation
Xie, F. and Wei, H. (2023), "BesNet: binocular ferrographic image recognition model based on deep learning technology", Industrial Lubrication and Tribology, Vol. 75 No. 6, pp. 714-720. https://doi.org/10.1108/ILT-05-2023-0150
Publisher
:Emerald Publishing Limited
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