Runtime Observer Pairs and Bayesian Network Reasoners On-board FPGAs: Flight-Certifiable System Health Management for Embedded Systems
Runtime Observer Pairs and Bayesian Network Reasoners On-board FPGAs: Flight-Certifiable System Health Management for Embedded Systems
复制标题
FPGA 上的运行时观察器对和贝叶斯网络推理器：嵌入式系统的飞行认证系统健康管理
DOI：
10.1007/978-3-319-11164-3_18
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发表时间：
2014
期刊：
影响因子：
--
通讯作者：
Johann Schumann
中科院分区：
文献类型：
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作者：
J. Geist;Kristin Y. Rozier;Johann Schumann
安全关键系统，例如必须完全自主运行的无人机系统 (UAS)，例如支持地面应急服务，还必须保证它们不会危及空中或地面的人员生命或财产。此前，介绍了采用贝叶斯推理的配对同步和异步运行时观察器的理论构造，证明了在系统必须遵守的严格操作约束内处理运行时保证的能力。在本文中，我们展示了如何实例化和实现时态逻辑运行时观察器和贝叶斯网络诊断推理器，这些观察器和贝叶斯网络诊断推理器使用观察器的输出，板载现场标准现场可编程门阵列（FPGA），以满足严格的可实现性、响应性和不显眼的飞行操作标准。通过这种组合构建的诊断框架，我们可以开发紧凑、分层且高度表达的健康管理模型，以实现高效的板载故障检测和系统监控。我们描述了在标准 FPGA 硬件上的系统健康管理 (SHM) 框架 rt-R2U2 的实例，该框架适合部署在 UAS 上。我们使用来自 NASA Swift UAS 的全套真实飞行数据来运行我们的系统，并重点介绍了一个案例，即我们的运行时 SHM 框架能够从最初逃避传统实时诊断程序的微妙证据中检测和诊断故障。
Safety-critical systems, like Unmanned Aerial Systems (UAS) that must operate totally autonomously, e.g., to support ground-based emergency services, must also provide assurance they will not endanger human life or property in the air or on the ground. Previously, a theoretical construction for paired synchronous and asynchronous runtime observers with Bayesian reasoning was introduced that demonstrated the ability to handle runtime assurance within the strict operational constraints to which the system must adhere. In this paper, we show how to instantiate and implement temporal logic runtime observers and Bayesian network diagnostic reasoners that use the observers’ outputs, on-board a field-standard Field Programmable Gate Array (FPGA) in a way that satisfies the strict flight operational standards of Realizability, Responsiveness, and Unobtrusiveness. With this type of compositionally constructed diagnostics framework we can develop compact, hierarchical, and highly expressive health management models for efficient, on-board fault detection and system monitoring. We describe an instantiation of our System Health Management (SHM) framework, rt-R2U2, on standard FPGA hardware, which is suitable to be deployed on-board a UAS. We run our system with a full set of real flight data from NASA’s Swift UAS, and highlight a case where our runtime SHM framework would have been able to detect and diagnose a fault from subtle evidence that initially eluded traditional real-time diagnosis procedures.