A novel tool condition monitoring based on Gramian angular field and comparative learning

IF 5.3 Q1 ENGINEERING, MECHANICAL
Hongche Wang, Wei Sun, Weifang Sun, Yan Ren, Yuqing Zhou, Qijia Qian, Anil Kumar
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引用次数: 4

Abstract

Accurate tool condition monitoring (TCM) is an important part for ensuring milling quality. However, due to the cost of TCM experiment, there are few labelled and a lot of unlabelled samples in the training set that significantly affect the accuracy of many machine learning models. A novel method based on comparative learning (CL) and Gramian angular field (GAF) is proposed for improving the performance of TCM. The cutting force signals of each channel of all samples (including labelled and unlabelled) collected in TCM experiment are expanded to grey images by GAF, and combined with other channels to a colour image. Then, these colour images are input to the CL pre-training model to learn features. Finally, the extracted features and the few labelled samples are applied to train the ResNet18 model to obtain excellent classification results. The milling TCM experiments show that the classification precision of the proposed GAF-CL model is above 95% with small labelled samples, which is more than 19% higher than the ImageNet pre-training model.
一种基于Gramian角场和比较学习的刀具状态监测方法
准确的刀具状态监测是保证铣削质量的重要环节。然而,由于中医实验的成本,训练集中有标记的样本很少,而未标记的样本很多,这严重影响了许多机器学习模型的准确性。提出了一种基于比较学习(CL)和Gramian角场(GAF)的新方法来提高TCM的性能。TCM实验中采集的所有样品(包括有标记和未标记的样品)的每个通道的切割力信号通过GAF扩展为灰度图像,并与其他通道合并为彩色图像。然后,将这些彩色图像输入到CL预训练模型中学习特征。最后,将提取的特征和少量标记的样本应用于ResNet18模型的训练,获得优异的分类效果。铣削中医实验表明,在标记样本较小的情况下,本文提出的GAF-CL模型的分类精度在95%以上,比ImageNet预训练模型提高了19%以上。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
7.60
自引率
0.00%
发文量
32
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