...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Symmetry >Fault Detection Based on Multi-Scale Local Binary Patterns Operator and Improved Teaching-Learning-Based Optimization Algorithm
【24h】

Fault Detection Based on Multi-Scale Local Binary Patterns Operator and Improved Teaching-Learning-Based Optimization Algorithm

机译：基于多尺度局部二值模式算子和改进的基于学习-学习的优化算法的故障检测

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

摘要

Aiming to effectively recognize train center plate bolt loss faults, this paper presents an improved fault detection method. A multi-scale local binary pattern operator containing the local texture information of different radii is designed to extract more efficient discrimination information. An improved teaching-learning-based optimization algorithm is established to optimize the classification results in the decision level. Two new phases including the worst recombination phase and the cuckoo search phase are incorporated to improve the diversity of the population and enhance the exploration. In the worst recombination phase, the worst solution is updated by a crossover recombination operation to prevent the premature convergence. The cuckoo search phase is adopted to escape the local optima. Experimental results indicate that the recognition accuracy is up to 98.9% which strongly demonstrates the effectiveness and reliability of the proposed detection method.
机译：为了有效识别列车中心板螺栓丢失故障，本文提出了一种改进的故障检测方法。包含不同半径的局部纹理信息的多尺度局部二进制模式算子被设计为提取更有效的判别信息。建立了一种改进的基于教学的优化算法，以优化决策层的分类结果。结合了两个新阶段，包括最坏的重组阶段和布谷鸟搜索阶段，以改善种群的多样性并加强勘探。在最坏的重组阶段，最坏的解决方案将通过交叉重组操作进行更新，以防止过早收敛。布谷鸟搜索阶段被采用以逃避局部最优。实验结果表明，识别精度高达98.9％，这充分证明了所提出检测方法的有效性和可靠性。

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