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首页> 外文会议>International Conference on Cybernetics and Information Technologies, Systems and Applications(CITSA2004) and 10th International Conference on Information Systems Analysis and Synthesis(ISAS2004) vol.1 >Generated Model-based Reasoning Architectures through Dynamic Simulation
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Generated Model-based Reasoning Architectures through Dynamic Simulation

机译：通过动态仿真生成基于模型的推理架构

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摘要

In recent years, a significant focus has been placed on the development and implementation of advanced prognostic and health management (PHM) technologies for air vehicles both in military and industrial applications. The term PHM encompasses anomaly, diagnostic and prognostic algorithms as well as higher level reasoning algorithms for isolating root causes of faults/failures and directing optimal operational or maintenance actions. In such systems, two current deficiencies exist. First, for a variety of reasons, component and subsystem interactions in such systems are poorly realized. The issue manifests itself as multiple dependent "boxes" indicating faults with shotgun tests or valuable domain expertise required to de-conflict and reduce ambiguity groups. Secondly, complex systems still largely rely on expert rule-bases for reasoning which are notoriously difficult to maintain over a life cycle and are prone to logical conflicts. This paper begins to address these deficiencies by outlining a simulation-based process for automatically 1) realizing complex system interactions for optimal PHM system design and 2) building and maintaining model-based reasoning architectures where decisions and conclusions naturally precipitate out of a more manageable system dependency model. Concepts are reinforced and demonstrated with a Matlab/Simulink example of a complex dynamic system and an analysis test bench for reporting system wide dependencies, fault propagation and consolidated model-based reasoning architectures.
机译：近年来，在军事和工业应用中，航空器的高级预后和健康管理（PHM）技术的开发和实施已成为重点。 PHM一词涵盖了异常，诊断和预后算法，以及用于隔离故障/故障的根本原因并指导最佳操作或维护措施的高级推理算法。在这样的系统中，存在两个当前的缺陷。首先，由于各种原因，这种系统中的组件和子系统之间的交互作用很差。该问题表现为多个从属的“框”，这些框表示gun弹枪测试的错误或消除冲突和减少歧义性组所需的宝贵领域专业知识。其次，复杂的系统仍然在很大程度上依赖专家规则库进行推理，这在整个生命周期中都很难维护，并且容易发生逻辑冲突。本文通过概述基于仿真的过程来自动解决这些缺陷，这些过程可自动进行以下操作：1）实现最佳PHM系统设计的复杂系统交互，以及2）建立和维护基于模型的推理体系，其中决策和结论自然会从更易于管理的系统中沉淀出来依赖模型。通过复杂的动态系统的Matlab / Simulink示例和用于报告系统范围的依赖关系，故障传播和基于模型的合并推理架构的分析测试台，对概念进行了增强和演示。

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