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A Partial Hierarchical Model for Online Low-Resolution Wear Particle Images Classification
Computational Intelligence and Neuroscience ( IF 3.120 ) Pub Date : 2021-04-14 , DOI: 10.1155/2021/6630247
Xuxu Guo 1, 2 , Rui Tan 3 , Mingyang Yang 4 , Xinrong He 3 , Jia Guo 3 , Suli Fan 1, 2 , Junnan Hu 1, 2 , Taohong Zhang 1, 2 , Aziguli Wulamu 1, 2
Affiliation  

Wear particle image analysis is an effective method to detect wear condition of mechanical devices. However, the recognition accuracy and recognition efficiency for online wear particle automatic recognition are always mutual restricted because the online wear particle images have almost no texture information and lack clarity. Especially for confusing fatigue wear particles and sliding wear particles, the online recognition is a challenging task. Based on this requirement, a super-resolution reconstruct technique and partial hierarchical convolutional neural network, SR-PHnet, is proposed to classify wear particles in one step. The structure of this network is composed by three modules, one is super-resolution layer module, the second is convolutional neural network classifier module, and the third is support vector machine (SVM) classifier module. The classification result of the second module is partial input to the third module for precision classification of fatigue and sliding particles. In addition, a new feature of radial edge factor (REF) is put forward to target fatigue and sliding wear particles. The test result shows that the new feature has the capability to distinguish fatigue and sliding particles well and time saving. The comparison experiments of the convolution neural network (CNN) method, support vector machine method (SVM) with and without REF feature, and integrated model of back-propagation (BP) and CNN are produced. The comparison results show that the online recognition speed and online recognition rate of the proposed SR-PHnet model in this paper are both improved markedly, especially for fatigue and sliding wear particles.

中文翻译:

在线低分辨率磨损颗粒图像分类的局部层次模型

磨损颗粒图像分析是检测机械设备磨损状况的有效方法。然而,由于在线磨损颗粒图像几乎没有纹理信息并且缺乏清晰度,因此在线磨损颗粒自动识别的识别精度和识别效率始终相互制约。尤其是对于混淆疲劳磨损颗粒和滑动磨损颗粒,在线识别是一项艰巨的任务。基于这一要求,提出了一种超分辨率重建技术和部分层次卷积神经网络SR-PHnet,可以对磨损颗粒进行一步分类。该网络的结构由三个模块组成,一个是超分辨率层模块,第二个是卷积神经网络分类器模块,第三个是支持向量机(SVM)分类器模块。第二模块的分类结果部分输入到第三模块,以精确分类疲劳和滑动颗粒。此外,针对边缘疲劳和滑动磨损颗粒提出了径向边缘系数(REF)的新功能。测试结果表明,该新功能能够很好地识别疲劳和滑动颗粒,并节省时间。进行了卷积神经网络(CNN)方法,具有和不具有REF功能的支持向量机方法(SVM)以及反向传播(BP)和CNN的集成模型的比较实验。比较结果表明,本文提出的SR-PHnet模型的在线识别速度和在线识别率均得到明显提高,
更新日期:2021-04-14
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