...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability >Component reliability in fault-diagnosis decision making based on dynamic Bayesian networks
【24h】

Component reliability in fault-diagnosis decision making based on dynamic Bayesian networks

机译：基于动态贝叶斯网络的故障诊断决策中的组件可靠性

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The decision making in fault diagnosis methods generally relies on the analysis of fault signature vectors. The current paper presents a new approach of decision making for the signature vectors for various identical or similar faults. The main contribution of the paper consists in the fusion between the reliability and the evaluation of the residuals in order to increase the fault isolation efficiency. The decision making, formalized as a Bayesian network, is established with a priori knowledge on fault signatures, false alarm and missing detection probability, online component state estimation computed by a Bayesian fusion of the component reliability, and measurements. The effectiveness and performances of the method are illustrated on a heating water process corrupted by various faults.
机译：故障诊断方法中的决策通常依赖于故障特征向量的分析。本文提出了一种针对各种相同或相似故障的特征向量决策的新方法。本文的主要贡献在于可靠性与残差评估之间的融合，以提高故障隔离效率。该决策的制定被正式化为贝叶斯网络，是基于以下方面的先验知识：故障特征，错误警报和丢失检测概率，通过组件可靠性的贝叶斯融合计算的在线组件状态估计以及测量。在由于各种故障而损坏的热水过程中，说明了该方法的有效性和性能。

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