...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Mathematical Problems in Engineering: Theory, Methods and Applications >An Enhanced Artificial Bee Colony-Based Support Vector Machine for Image-Based Fault Detection
【24h】

An Enhanced Artificial Bee Colony-Based Support Vector Machine for Image-Based Fault Detection

机译：改进的基于人工蜂群的支持向量机在基于图像的故障检测中的应用

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Fault detection has become extremely important in industrial production so that numerous potential losses caused from equipment failures could be saved. As a noncontact method, machine vision can satisfy the needs of real-time fault monitoring. However, image-based fault features often have the characteristics of high-dimensionality and redundant correlation. To optimize feature subsets and SVM parameters, this paper presents an enhanced artificial bee colony-based support vector machine (EABC-SVM) approach. The method is applied to the image-based fault detection for the conveyor belt. To improve the optimized capability of original ABC, the EABC algorithm introduces two enhanced strategies including the Cat chaotic mapping initialization and current optimum based search equations. Several UCI datasets have been used to evaluate the performance of EABC-SVM and the experimental results show that this approach has better classification accuracy and convergence performance than the ABC-SVM and other ABC variants-based SVM. Furthermore, the EABC-SVM can achieve a significant detection accuracy of 95% and reduce the amount of features about 65% in the conveyor belt fault detection.
机译：故障检测在工业生产中已变得极为重要，因此可以节省由于设备故障引起的大量潜在损失。作为一种非接触式方法，机器视觉可以满足实时故障监控的需求。但是，基于图像的故障特征通常具有高维和冗余相关性的特征。为了优化特征子集和SVM参数，本文提出了一种改进的基于人工蜂群的支持向量机（EABC-SVM）方法。该方法应用于传送带的基于图像的故障检测。为了提高原始ABC的优化能力，EABC算法引入了两种增强的策略，包括Cat混沌映射初始化和基于当前最优的搜索方程。几个UCI数据集已用于评估EABC-SVM的性能，实验结果表明，此方法比ABC-SVM和其他基于ABC变体的SVM具有更好的分类准确性和收敛性能。此外，EABC-SVM可以实现95％的显着检测精度，并减少传送带故障检测中约65％的特征量。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号