• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IFAC PapersOnLine >Robust neural-network-based fault detection with sequential D-optimum bounded-error input design
【24h】

Robust neural-network-based fault detection with sequential D-optimum bounded-error input design

机译：具有顺序D最佳有界误差输入设计的基于神经网络的鲁棒故障检测

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

摘要

A growing demand for technologically advanced systems has contributed to the increase of the awareness of systems safety and reliability. Such a situation requires the development of novel methods of robust fault diagnosis. The application of the analytical redundancy based methods for system fault detection causes that their effectiveness depends on model quality. In this paper, a new methodology for the improvement of the neural model with a D-optimum sequential experimental design technique combined with outer bounding ellipsoid algorithm is proposed. Moreover, a novel method of robust fault detection against neural model uncertainty and disturbances is developed. Such an approach is used for modelling and robust fault detection of the three-screw spindle oil pump.
机译：对技术先进系统的需求不断增长，促使人们对系统安全性和可靠性有了更高的认识。这种情况需要开发出鲁棒故障诊断的新方法。基于分析冗余的系统故障检测方法的应用导致其有效性取决于模型质量。本文提出了一种新的方法，该方法利用D-最优顺序实验设计技术与外边界椭球算法相结合来改进神经模型。此外，开发了一种针对神经模型不确定性和干扰的鲁棒故障检测新方法。这种方法用于三螺杆主轴油泵的建模和可靠的故障检测。

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