Dynamic data-driven learning for self-healing avionics
Dynamic data-driven learning for self-healing avionics
复制标题
用于自我修复航空电子设备的动态数据驱动学习
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;Sida Chen;Wennan Zhu;Carlos A. Varela
在基于传感器的系统中，时空数据流通常以非平凡的方式相关。例如，在航空电子设备中，虽然飞机在巡航阶段达到的空速取决于其携带的重量，但它还取决于许多其他因素，例如发动机输入、迎角和空气密度。因此，开发能够帮助识别数据错误（例如不正确的燃油量或不正确的空速）的故障模型是一项挑战。在本文中，我们提出了一个高度声明性的编程框架，该框架有助于开发自我修复的航空电子应用程序，该应用程序可以检测数据错误并从中恢复。我们的编程框架允许使用错误签名指定专家创建的故障模型，以及从数据中学习故障模型。为了解决意外的故障模式，我们提出了一种新的动态贝叶斯分类器，它可以检测异常值，并在统计显着时将其升级到新模式。我们评估错误签名和动态贝叶斯分类器的准确性、响应时间和错误检测的适应性。虽然错误签名比动态贝叶斯学习更准确、响应更灵敏，但后一种方法由于其数据驱动的性质而适应得更好。
In sensor-based systems, spatio-temporal data streams are often related in non-trivial ways. For example in avionics, while the airspeed that an aircraft attains in cruise phase depends on the weight it carries, it also depends on many other factors such as engine inputs, angle of attack, and air density. It is therefore a challenge to develop failure models that can help recognize errors in the data, such as an incorrect fuel quantity or an incorrect airspeed. In this paper, we present a highly-declarative programming framework that facilitates the development of self-healing avionics applications, which can detect and recover from data errors. Our programming framework enables specifying expert-created failure models using error signatures, as well as learning failure models from data. To account for unanticipated failure modes, we propose a new dynamic Bayes classifier, that detects outliers and upgrades them to new modes when statistically significant. We evaluate error signatures and our dynamic Bayes classifier for accuracy, response time, and adaptability of error detection. While error signatures can be more accurate and responsive than dynamic Bayesian learning, the latter method adapts better due to its data-driven nature.