Self-Healing Data Streams Using Multiple Models of Analytical Redundancy
Self-Healing Data Streams Using Multiple Models of Analytical Redundancy
复制标题
使用多种分析冗余模型的自我修复数据流
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Carlos A. Varela
中科院分区：
文献类型：
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作者：
Shigeru Imai;F. Hole;Carlos A. Varela
我们已经创建了一种称为PILOTS的高度声明性编程语言，它能够基于分析冗余（即，数据流之间的代数关系）。数据科学家能够用PILOTS的领域特定语法来表达他们的分析冗余模型，并用错误的数据流来测试他们的模型。PILOTS具有表达单个分析冗余的能力，并且它已成功地应用于来自飞机事故的数据，例如法航447航班和Tuninter 1153航班，其中仅观察到一个同时发生的传感器类型故障。在这项工作中，我们扩展PILOTS，以支持多种模型的分析冗余，并提高态势感知多个同时传感器类型的故障。受最近两起涉及波音737 Max 8的事故的启发，这两起事故可能是由迎角传感器故障引起的，我们专注于在多个传感器类型故障情况下恢复迎角数据流。仿真结果表明，多个模型的分析冗余，使我们能够检测到的故障模式，是无法检测到一个单一的模型。
We have created a highly declarative programming language called PILOTS that enables error detection and estimation of correct data streams based on analytical redundancy (i.e., algebraic relationship between data streams). Data scientists are able to express their analytical redundancy models with the domain specific grammar of PILOTS and test their models with erroneous data streams. PILOTS has the ability to express a single analytical redundancy, and it has been successfully applied to data from aircraft accidents such as Air France flight 447 and Tuninter flight 1153 where only one simultaneous sensor type failure was observed. In this work, we extend PILOTS to support multiple models of analytical redundancy and improve situational awareness for multiple simultaneous sensor type failures. Motivated by the two recent accidents involving the Boeing 737 Max 8, which was potentially caused by a faulty angle of attack sensor, we focus on recovering angle of attack data streams under multiple sensor type failure scenarios. The simulation results show that multiple models of analytical redundancy enable us to detect failure modes that are not detectable with a single model.