基于涡流传感器的线材生产表面缺陷无监督分类

Unsupervised classification of surface defects in wire rod production obtained by eddy current sensors.

作者信息

Saludes-Rodil Sergio, Baeyens Enrique, Rodríguez-Juan Carlos P

机构信息

Centro Tecnológico CARTIF, Parque Tecnológico de Boecillo 205, 47151 Boecillo, Valladolid, Spain.

Instituto de las Tecnologías Avanzadas de la Producción, Universidad de Valladolid, Paseo del cauce 59, 47011 Valladolid, Spain.

出版信息

Sensors (Basel). 2015 Apr 29;15(5):10100-17. doi: 10.3390/s150510100.

DOI:10.3390/s150510100

PMID:25938201

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4482004/

Abstract

An unsupervised approach to classify surface defects in wire rod manufacturing is developed in this paper. The defects are extracted from an eddy current signal and classified using a clustering technique that uses the dynamic time warping distance as the dissimilarity measure. The new approach has been successfully tested using industrial data. It is shown that it outperforms other classification alternatives, such as the modified Fourier descriptors.

摘要

本文开发了一种用于对线材制造中的表面缺陷进行分类的无监督方法。缺陷从涡流信号中提取，并使用一种聚类技术进行分类，该技术将动态时间规整距离用作相异度度量。新方法已使用工业数据成功进行了测试。结果表明，它优于其他分类方法，如改进的傅里叶描述符。