...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of Quality Technology >Incorporation of process-specific structure in statistical process monitoring: A review
【24h】

Incorporation of process-specific structure in statistical process monitoring: A review

机译：在统计过程监测中纳入流程特定结构：审查

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 incorporation of process-specific structure in monitoring activities has the potential to improve fault detection and fault diagnosis in modern industrial scenarios. By including causal information in the normal operation conditions (NOC) models, more effective use of the large amount of data can be made, leading to the detection of finer deviations from normal behavior. Furthermore, embedded in these methods is the fundamental cause effect information necessary to efficiently guide fault diagnosis and troubleshooting activities. This capability is absent from classical process monitoring methods based on acausal (or non-causal) NOC models, limiting to a large extent their fault diagnosis performance. In this context, a variety of approaches incorporating process-specific structure have emerged in the applied statistics, engineering, and machine learning communities and, in this article, we propose a classification and provide a systematic review of this developing trend in process monitoring. We also discuss the main features and limitations of each category of methods, propose a mapping for defining their application contexts and illustrate their use with an example.
机译：在监测活动中纳入特定于过程特定结构有可能改善现代工业情景中的故障检测和故障诊断。通过在正常操作条件（NOC）模型中包括因果信息，可以更有效地使用大量数据，从而导致从正常行为中检测更精细的偏差。此外，在这些方法中嵌入到有效指导故障诊断和故障排除活动所需的基本原因效果信息。基于ACAUSAL（或非因果）NOC模型的古典过程监测方法，这种能力不存在，在很大程度上限制了它们的故障诊断性能。在这种情况下，在应用统计，工程和机器学习社区中出现了一种加入过程特定结构的各种方法，并且在本文中，我们提出了一个分类，并在过程监测中提出了对这种发展趋势的系统审查。我们还讨论了每种类别的方法的主要特征和限制，提出了一个映射，用于定义其应用程序上下文并说明它们与示例的用途。

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